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dkNET Webinar: Tabula Sapiens 03/22/2024

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Presenter: Angela Oliveira Pisco , PhD Abstract Although the genome is often called the blueprint of an organism, it is perhaps more accurate to describe it as a parts list composed of the various genes that may or may not be used in the different cell types of a multicellular organism. While nearly every cell in the body has essentially the same genome, each cell type makes different use of that genome and expresses a subset of all possible genes. This has motivated efforts to characterize the molecular composition of various cell types within humans and multiple model organisms, both by transcriptional and proteomic approaches. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. One caveat to current approaches to make cell atlases is that individual organs are often collected at different locations, collected from different donors, and processed using different protocols. Controlled comparisons of cell types between different tissues and organs are especially difficult when donors differ in genetic background, age, environmental exposure, and epigenetic effects. To address this, we developed an approach to analyzing large numbers of organs from the same individual. We collected multiple tissues from individual human donors and performed coordinated single-cell transcriptome analyses on live cells. The donors come from a range of ethnicities, are balanced by gender, have a mean age of 51 years, and have a variety of medical backgrounds. Tissue experts used a defined cell ontology terminology to annotate cell types consistently across the different tissues, leading to a total of 475 distinct cell types with reference transcriptome profiles. The Tabula Sapiens also provided an opportunity to densely and directly sample the human microbiome throughout the gastrointestinal tract. The Tabula Sapiens has revealed discoveries relating to shared behavior and subtle, organ-specific differences across cell types. We found T cell clones shared between organs and characterized organ-dependent hypermutation rates among B cells. Endothelial cells and macrophages are shared across tissues, often showing subtle but clear differences in gene expression. We found an unexpectedly large and diverse amount of cell type–specific RNA splice variant usage and discovered and validated many previously undefined splices. The intestinal microbiome was revealed to have nonuniform species distributions down to the 3-inch (7.62-cm) length scale. These are but a few examples of how the Tabula Sapiens represents a broadly useful reference...Full abstract: https://dknet.org/about/blog/2726 Resource link: https://tabula-sapiens-portal.ds.czbiohub.org Upcoming webinars schedule: https://dknet.org/about/webinar

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Angela Oliveira Pisco angela.oliveira.pisco@gmail.com @drAOPisco @aopisco Tabula Sapiens A multiple-organ, single-cell transcriptomic atlas of humans 03-22-2024
WHOLE ORGANISM CELL ATLASES 2 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
WHOLE ORGANISM CELL ATLASES 2 understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
WHOLE ORGANISM CELL ATLASES 2 Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + 1. Why single cells? Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + Cells 1. Why single cells? Organ/Tissue Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + Cells 1. Why single cells? Organ/Tissue Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + Cells 1. Why single cells? Organ/Tissue Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + Cells 1. Why single cells? 2. Why transcriptomics? Organ/Tissue Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + In multicellular organisms, nearly every cell contains the same genome but not every gene is transcriptionally active in every cell Transcriptome, the total amount of RNA, offers a closer view of the real time gene expression in a cell Cells 1. Why single cells? 2. Why transcriptomics? Organ/Tissue Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
SINGLE CELL DATA COLLECTION WORKFLOW 3 The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
SINGLE CELL DATA COLLECTION WORKFLOW 3 Organ procurement The Tabula Sapiens Consortium, Science (2022) Partnership with Donor Network West Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
SINGLE CELL DATA COLLECTION WORKFLOW 3 Organ procurement Cell dissociation The Tabula Sapiens Consortium, Science (2022) Organ speci fi c teams Partnership with Donor Network West Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
SINGLE CELL DATA COLLECTION WORKFLOW 3 Organ procurement Cell dissociation 10x genomics Illumina sequencing Smartseq-2 Data analysis The Tabula Sapiens Consortium, Science (2022) Organ speci fi c teams Centralized Partnership with Donor Network West Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
SC-RNA SEQ DATA PROCESSING 4 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
SC-RNA SEQ DATA PROCESSING 4 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
SC-RNA SEQ DATA PROCESSING 4 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature UMAP1 UMAP2 Compartment endothelial epithelial germ line immune stromal UMAP1 UMAP2 Donor TSP1 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 TSP10 TSP11 TSP12 TSP13 TSP14 TSP15 10X smartseq2 UMAP1 UMAP2 Method 10X smartseq2 UMAP1 UMAP2 Gender female male Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
DATASET SUMMARY 5 Clinical metadata Age Gender Ethnicity Race BMI (kg/m2) Downtime (min) Cause of DeathTobacco (>20 pack-years) Alcohol IV Drug abuse Diabetes Cancer Hypertension Coronary Artery Disease Other noted conditions The Tabula Sapiens Consortium, Science (2022) TSP1 TSP2 TSP7 TSP14 Additional 11 Donors Bladder Blood Bone Marrow Eye Fat Heart Kidney Large Intestine Liver Lung Lymph Node Mammary Gland Muscle Pancreas Prostate Salivary Gland Skin Small Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Total 483,152 cells 24,583 50,115 12,297 10,650 20,263 11,505 9,641 13,680 5,007 35,682 53,275 11,375 30,746 13,497 16,375 27,199 9,424 12,467 34,004 33,664 15,020 9,522 7,124 16,037 6000 12000 18000 24000 30000 Cell count Number of cells Fig. 1 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (I) 6 The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (I) 6 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature
CELL TYPE ANNOTATION (I) 6 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes Unannotated dataset Scanorama BBKNN scVI Random Forest SVM OnClass scANVI Unsupervised Method + kNN Supervised Method Semi-supervised Method PopularVote ANNOTATION Expert Manual Partial Annotation manually annotated dataset Cells Genes Expert Manual Annotation Revision computer automated annotated dataset Cells Genes annotated reference dataset Cells Genes The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature
CELL TYPE ANNOTATION (II) 7 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (II) 7 Reference Tabula Sapiens Draft reference Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (II) 7 Reference *new* Tabula Sapiens subset Tabula Sapiens Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (II) 7 Reference *new* Tabula Sapiens subset Tabula Sapiens Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (II) 7 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (II) 7 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Expert curation Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (II) 8 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Expert curation Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (II) 8 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Expert curation Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
9 PopV (I) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
9 PopV (I) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
9 PopV (I) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
9 PopV (I) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
10 PopV (II) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
10 PopV (II) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
11 PopV (III) https://tabula-sapiens-portal.ds.czbiohub.org/annotateuserdata https://github.com/YosefLab/PopV Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
11 PopV (III) https://tabula-sapiens-portal.ds.czbiohub.org/annotateuserdata https://github.com/YosefLab/PopV Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
11 PopV (III) https://tabula-sapiens-portal.ds.czbiohub.org/annotateuserdata https://github.com/YosefLab/PopV Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (III) 12 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Expert curation Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CELL TYPE ANNOTATION (III) 12 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Expert curation Tabula Sapiens publication New Reference Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
BUILDING A REFERENCE ATLAS 13 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes Unannotated dataset Scanorama BBKNN scVI Random Forest SVM OnClass scANVI Unsupervised Method + kNN Supervised Method Semi-supervised Method PopularVote ANNOTATION Expert Manual Partial Annotation manually annotated dataset Cells Genes Expert Manual Annotation Revision computer automated annotated dataset Cells Genes annotated reference dataset Cells Genes Automated annotations Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
BUILDING A REFERENCE ATLAS 13 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes Unannotated dataset Scanorama BBKNN scVI Random Forest SVM OnClass scANVI Unsupervised Method + kNN Supervised Method Semi-supervised Method PopularVote ANNOTATION Expert Manual Partial Annotation manually annotated dataset Cells Genes Expert Manual Annotation Revision computer automated annotated dataset Cells Genes annotated reference dataset Cells Genes 475 cell types curation by tissue experts Automated annotations Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
14 MARKER GENES FOR CELL TYPES Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
14 MARKER GENES FOR CELL TYPES Wang, Pisco, et al, Nat Comms (2021) https://tabula-sapiens-portal.ds.czbiohub.org/markergenes Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 15 UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 15 Macrophages UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 16 Macrophages Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS n = 36,475 (broad) macrophage cell ontology class across 20 tissues 16 Macrophages Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
A L u n g S p l e e n L i v e r T r a c h e a T h y m u s E y e F a t P r o s t a t e H e a r t K i d n e y B l o o d L y m p h N o d e V a s c u l a t u r e S a l i v a r y G l a n d B o n e M a r r o w B l a d d e r M u s c l e S k i n M a m m a r y U t e r u s Lung Lymph Node Spleen Liver Trachea Thymus Vasculature Eye Fat Prostate Salivary Gland Heart Kidney Blood Bone Marrow Bladder Muscle Skin Mammary Uterus −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Pearson correlation Fig. S11 B Bladder Blood Bone_Marrow Eye Fat Heart Kidney Liver Lung Lymph_Node Mammary Muscle Prostate Salivary_Gland CROSS-TISSUE ANALYSIS n = 36,475 (broad) macrophage cell ontology class across 20 tissues 16 Macrophages Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
A L u n g S p l e e n L i v e r T r a c h e a T h y m u s E y e F a t P r o s t a t e H e a r t K i d n e y B l o o d L y m p h N o d e V a s c u l a t u r e S a l i v a r y G l a n d B o n e M a r r o w B l a d d e r M u s c l e S k i n M a m m a r y U t e r u s Lung Lymph Node Spleen Liver Trachea Thymus Vasculature Eye Fat Prostate Salivary Gland Heart Kidney Blood Bone Marrow Bladder Muscle Skin Mammary Uterus −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Pearson correlation Fig. S11 B Bladder Blood Bone_Marrow Eye Fat Heart Kidney Liver Lung Lymph_Node Mammary Muscle Prostate Salivary_Gland CROSS-TISSUE ANALYSIS n = 36,475 (broad) macrophage cell ontology class across 20 tissues 16 Macrophages r o w e r M u s c l e S k i n M a m m a r y U t e r u s Lung Lymph Node Spleen Liver Trachea Thymus Vasculature Eye Fat Prostate Salivary Gland Heart Kidney Blood Bone Marrow Bladder Muscle Skin Mammary Uterus .75 1.00 Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Legend for panels C (bottom) and D TSP1 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 TSP10 TSP11 TSP12 TSP13 TSP14 TSP15 Legend for panel C (top) D 0.0 0.1 0.2 Fat Skin Bone_Marrow Trachea Liver Eye Spleen Lung Kidney Lymph_Node Blood Salivary_Gland Tongue Tissue B 0 1 2 Median expression in group CHIT1 EREG CTSK CD5L Bladder Blood Bone_Marrow Eye Fat Heart Kidney Liver Lung Lymph_Node Mammary Muscle Prostate Salivary_Gland Skin Spleen Thymus Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
A L u n g S p l e e n L i v e r T r a c h e a T h y m u s E y e F a t P r o s t a t e H e a r t K i d n e y B l o o d L y m p h N o d e V a s c u l a t u r e S a l i v a r y G l a n d B o n e M a r r o w B l a d d e r M u s c l e S k i n M a m m a r y U t e r u s Lung Lymph Node Spleen Liver Trachea Thymus Vasculature Eye Fat Prostate Salivary Gland Heart Kidney Blood Bone Marrow Bladder Muscle Skin Mammary Uterus −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Pearson correlation Fig. S11 B Bladder Blood Bone_Marrow Eye Fat Heart Kidney Liver Lung Lymph_Node Mammary Muscle Prostate Salivary_Gland CROSS-TISSUE ANALYSIS n = 36,475 (broad) macrophage cell ontology class across 20 tissues 16 Macrophages r o w e r M u s c l e S k i n M a m m a r y U t e r u s Lung Lymph Node Spleen Liver Trachea Thymus Vasculature Eye Fat Prostate Salivary Gland Heart Kidney Blood Bone Marrow Bladder Muscle Skin Mammary Uterus .75 1.00 Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Legend for panels C (bottom) and D TSP1 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 TSP10 TSP11 TSP12 TSP13 TSP14 TSP15 Legend for panel C (top) D 0.0 0.1 0.2 Fat Skin Bone_Marrow Trachea Liver Eye Spleen Lung Kidney Lymph_Node Blood Salivary_Gland Tongue Tissue B 0 1 2 Median expression in group CHIT1 EREG CTSK CD5L Bladder Blood Bone_Marrow Eye Fat Heart Kidney Liver Lung Lymph_Node Mammary Muscle Prostate Salivary_Gland Skin Spleen Thymus Trachea Uterus Vasculature In macrophages, some genes are highly speci fi c to one tissue, others are speci fi c to multiple tissues, supporting the idea that tissue-resident macrophages are known to carry out specialized functions Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 17 UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 17 T cells UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 18 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 18 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 19 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 19 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 20 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes IgA IgM Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 20 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes B cells undergo class-switch recombination to diversify the humoral immune response IgA IgM Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 20 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes Thymus Eye Prostate Tongue Fat Skin 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 log 2 cpm B 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Fraction of Cells Large_Intestine Bladder Mammary Small_Intestine Eye Skin Pancreas Vasculature Uterus Lung Salivary_Gland Liver Lymph_Node Kidney Spleen Bone_Marrow Fat Thymus Trachea Blood Tissue IgA IgG IgM/D B cells undergo class-switch recombination to diversify the humoral immune response IgA IgM Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 20 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes Thymus Eye Prostate Tongue Fat Skin 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 log 2 cpm B 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Fraction of Cells Large_Intestine Bladder Mammary Small_Intestine Eye Skin Pancreas Vasculature Uterus Lung Salivary_Gland Liver Lymph_Node Kidney Spleen Bone_Marrow Fat Thymus Trachea Blood Tissue IgA IgG IgM/D B cells undergo class-switch recombination to diversify the humoral immune response typically expressed in naïve B cells or secreted in the fi rst response to pathogens interact with pathogens and commensals at the mucosa involved in direct neutralization of pathogens IgA IgM Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
CROSS-TISSUE ANALYSIS 20 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes Thymus Eye Prostate Tongue Fat Skin 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 log 2 cpm B 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Fraction of Cells Large_Intestine Bladder Mammary Small_Intestine Eye Skin Pancreas Vasculature Uterus Lung Salivary_Gland Liver Lymph_Node Kidney Spleen Bone_Marrow Fat Thymus Trachea Blood Tissue IgA IgG IgM/D B cells undergo class-switch recombination to diversify the humoral immune response typically expressed in naïve B cells or secreted in the fi rst response to pathogens interact with pathogens and commensals at the mucosa involved in direct neutralization of pathogens IgA IgM Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
21 CROSS-TISSUE ANALYSIS Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature
21 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature
22 ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Thymus Eye Prostate Tongue Fat Skin Uterus Liver Pancreas Bladder Mammary Salivary_Gland Vasculature Lung Muscle Heart 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 Tissue log2 cpm C 0.5 0.6 0.7 0.8 ells IgA IgG IgM/D Cell ontology class ANKRD29 LCN1 ABCG2 KR ANKRD29 LC ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2 ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA O ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CY ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CY VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VI ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart CROSS-TISSUE ANALYSIS Endothelial compartment Many of the endothelial markers are tissue-speci fi c while shared across donors Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
23 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
23 −10 −5 0 5 10 −5 0 5 10 UMAP 1 UMAP 2 Bladder Eye Fat Heart Liver Lung Mammary Muscle Pancreas Prostate Salivary_Gland Skin Thymus Tongue Uterus Vasculature Tissue −10 −5 0 5 10 −5 0 5 10 UMAP 1 TSP1 TSP10 TSP12 TSP14 TSP15 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 Donor UMAP 2 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
23 −10 −5 0 5 10 −5 0 5 10 UMAP 1 UMAP 2 Bladder Eye Fat Heart Liver Lung Mammary Muscle Pancreas Prostate Salivary_Gland Skin Thymus Tongue Uterus Vasculature Tissue −10 −5 0 5 10 −5 0 5 10 UMAP 1 TSP1 TSP10 TSP12 TSP14 TSP15 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 Donor UMAP 2 −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 4 PLVAP −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 EDNRB Lung CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
23 −10 −5 0 5 10 −5 0 5 10 UMAP 1 UMAP 2 Bladder Eye Fat Heart Liver Lung Mammary Muscle Pancreas Prostate Salivary_Gland Skin Thymus Tongue Uterus Vasculature Tissue −10 −5 0 5 10 −5 0 5 10 UMAP 1 TSP1 TSP10 TSP12 TSP14 TSP15 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 Donor UMAP 2 −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 4 PLVAP −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 EDNRB −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 4 MSX1 UMAP 2 −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 0 1 2 3 CYP1B1 Lung Muscle CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
23 −10 −5 0 5 10 −5 0 5 10 UMAP 1 UMAP 2 Bladder Eye Fat Heart Liver Lung Mammary Muscle Pancreas Prostate Salivary_Gland Skin Thymus Tongue Uterus Vasculature Tissue −10 −5 0 5 10 −5 0 5 10 UMAP 1 TSP1 TSP10 TSP12 TSP14 TSP15 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 Donor UMAP 2 −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 4 PLVAP −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 EDNRB −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 4 MSX1 UMAP 2 −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 0 1 2 3 CYP1B1 Lung Muscle CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
24 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead SLC14A1 appears to be a speci fi c marker for endothelial cells in the heart
24 Fig. S13 Heart muscle A B SLC14A1 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead SLC14A1 appears to be a speci fi c marker for endothelial cells in the heart
24 Fig. S13 Heart muscle A B SLC14A1 Heart muscle B SLC14A1 SLC14A1 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead SLC14A1 appears to be a speci fi c marker for endothelial cells in the heart Expression independently validated with data from the Human Protein Atlas!
ALTERNATIVE SPLICING (I) 25 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
ALTERNATIVE SPLICING (I) 25 annotated gene unannotated region A B C D E A: annotated junction B: Both boundaries are annotated but the splice is not C: One boundary is annotated and the other is not D: Neither boundary is annotated but at least one end is in a known gene E: Neither boundary is in a known gene Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Splice junctions are counted in categories based on annotation status Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
ALTERNATIVE SPLICING (I) 25 annotated gene unannotated region A B C D E A: annotated junction B: Both boundaries are annotated but the splice is not C: One boundary is annotated and the other is not D: Neither boundary is annotated but at least one end is in a known gene E: Neither boundary is in a known gene Fraction of junctions in each category 22.8% 3.6% 12.5% 57.9% 3.2% 955,785 junctions total A - 217,855 B - 34,624 C - 119,276 D - 553,345 E - 30,685 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Splice junctions are counted in categories based on annotation status Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
ALTERNATIVE SPLICING (I) 25 annotated gene unannotated region A B C D E A: annotated junction B: Both boundaries are annotated but the splice is not C: One boundary is annotated and the other is not D: Neither boundary is annotated but at least one end is in a known gene E: Neither boundary is in a known gene Fraction of junctions in each category 22.8% 3.6% 12.5% 57.9% 3.2% 955,785 junctions total A - 217,855 B - 34,624 C - 119,276 D - 553,345 E - 30,685 independent validation of 61% of the 358,924 total junctions catalogued in the RefSeq database! Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Splice junctions are counted in categories based on annotation status Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
ALTERNATIVE SPLICING (I) 25 annotated gene unannotated region A B C D E A: annotated junction B: Both boundaries are annotated but the splice is not C: One boundary is annotated and the other is not D: Neither boundary is annotated but at least one end is in a known gene E: Neither boundary is in a known gene Fraction of junctions in each category 22.8% 3.6% 12.5% 57.9% 3.2% 955,785 junctions total A - 217,855 B - 34,624 C - 119,276 D - 553,345 E - 30,685 95.7% 3.0% Fraction of reads in each category 1,633,557,320 junctions total A - 1,562,568,353 B - 7,291,982 C - 13,361,370 D - 49,308,361 E - 1,027,254 independent validation of 61% of the 358,924 total junctions catalogued in the RefSeq database! Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Splice junctions are counted in categories based on annotation status Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
ALTERNATIVE SPLICING (I) 25 annotated gene unannotated region A B C D E A: annotated junction B: Both boundaries are annotated but the splice is not C: One boundary is annotated and the other is not D: Neither boundary is annotated but at least one end is in a known gene E: Neither boundary is in a known gene Fraction of junctions in each category 22.8% 3.6% 12.5% 57.9% 3.2% 955,785 junctions total A - 217,855 B - 34,624 C - 119,276 D - 553,345 E - 30,685 95.7% 3.0% Fraction of reads in each category 1,633,557,320 junctions total A - 1,562,568,353 B - 7,291,982 C - 13,361,370 D - 49,308,361 E - 1,027,254 independent validation of 61% of the 358,924 total junctions catalogued in the RefSeq database! annotated junctions tend to be expressed at higher levels Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Splice junctions are counted in categories based on annotation status Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
ALTERNATIVE SPLICING (II) 26 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
ALTERNATIVE SPLICING (II) 26 4 5 exon #: 6 7 untranslated coding constitutive splice alternative splice MYL6 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
ALTERNATIVE SPLICING (II) 26 4 5 exon #: 6 7 untranslated coding constitutive splice alternative splice MYL6 -exon6 isoform previously described in phasic smooth muscle Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
ALTERNATIVE SPLICING (II) 26 4 5 exon #: 6 7 untranslated coding constitutive splice alternative splice MYL6 MYL6 splicing is differentially regulated across functionally different cell types like immune and endothelial -exon6 isoform previously described in phasic smooth muscle 0 1 e n d o t h e l i a l c e l l o f v a s c u l a r t r e e c a p i l l a r y e n d o t h e l i a l c e l l e n d o t h e l i a l c e l l o f a r t e r y v e i n e n d o t h e l i a l c e l l t y p e i i p n e u m o c y t e l u n g c i l i a t e d c e l l r e s p i r a t o r y g o b l e t c e l l b l a d d e r u r o t h e l i a l c e l l t y p e i p n e u m o c y t e m a c r o p h a g e n o n - c l a s s i c a l m o n o c y t e c l a s s i c a l m o n o c y t e m a c r o p h a g e m a c r o p h a g e s m o o t h m u s c l e c e l l p e r i c y t e c e l l f i b r o b l a s t p e r i c y t e c e l l m y o f i b r o b l a s t c e l l m e s e n c h y m a l s t e m c e l l t e n d o n c e l l s k e l e t a l m u s c l e s a t e l l i t e s t e m c e l l TSP1 0 1 Fraction including exon TSP2 0 1 TSP14 Bladder Lung Muscle p e i n o n - c l a s s i c c l a s s i c s m o o t h m y o f m e s e n c h y m s k e l e t a l m u s c l e s a t e Bladder Lung Muscle Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
ALTERNATIVE SPLICING (III) 27 D r e s p i r a t o r y g o b l e t c e l l b l a d d e r u r o t h e l i a l c e l l l u n g c TSP1 TSP2 TSP14 untranslated cytoplasmic transmembrane extracellular constitutive splice alternative splice 6 7 exon #: 9 10 8 11 CD47 C Epitheli Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
ALTERNATIVE SPLICING (III) 27 CD47 is another example of differential regulation of splicing for different cell types within an organ D r e s p i r a t o r y g o b l e t c e l l b l a d d e r u r o t h e l i a l c e l l l u n g c TSP1 TSP2 TSP14 untranslated cytoplasmic transmembrane extracellular constitutive splice alternative splice 6 7 exon #: 9 10 8 11 CD47 C Epitheli on 0 1 TSP14 D 7 8 9 10 r e s p i r a t o r y g o b l e t c e l l b l a d d e r u r o t h e l i a l c e l l l u n g c i l i a t e d c e l l t y p e i i p n e u m o c y t e c l u b c e l l t y p e i p n e u m o c y t e m a c r o p h a g e m a c r o p h a g e f i b r o b l a s t m e s e n c h y m a l s t e m c e l l TSP1 7 8 9 10 TSP2 7 8 9 10 TSP14 Average splice position 7 untranslated cytoplasmic transmembrane extracellular constitutive splice alternative splice 6 7 exon #: 9 10 8 11 CD47 C Endothelial Epithelial Immune Stromal Epithelial Immune Stromal e n d o t y p e i i p l u n g r e s p i r a t o r y b l a d d e r u r t y p e i p n m n o n - c l a s s i c a c l a s s i c a m m s m o o t h p p m y o f i b m e s e n c h y m a s k e l e t a l m u s c l e s a t e l l i Bladder Lung Muscle Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
BUILDING A MULTIMODAL DATASET (I) 28 Single cell & histology Organ procurement Single cell transcriptomics The Tabula Sapiens Consortium, Science (2022) UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
BUILDING A MULTIMODAL DATASET (I) 28 Single cell & histology Organ procurement Single cell transcriptomics Histopathology The Tabula Sapiens Consortium, Science (2022) UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead 29 H&E + SEQUENCING PNS/Neuroglial Endothelial 11. Endothelial cell (arteriole) 12. Endothelial cell (venule) 13. Endothelial cell (capillary) Stromal 14. Smooth muscle cell (arteriole) 15. Fibroblast 16. Ganglion cell (Meissner plexus) 17. Schwann cell Epithelial 1. Absorptive enterocyte 2. Goblet cell 3. Paneth cell 4. Crypt enterocyte 5. Enteroendocrine cell Endothelial 6. Endothelial cell (capillary or lymphatic) Stromal 7. Fibroblast Immune 8. Lymphocyte 9. Plasma cell 10. Eosinophil
Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead 29 Lymph Node Spleen Muscle Diaphragm Aorta Duodenum Ileum Colon (Proximal) Colon (Distal) Bladder Trachea Lung (Proximal) Lung (Distal) 0 20 40 60 80 100 Relative compartment abundacy (%) Spatial Heterogeneity low medium high H&E + SEQUENCING PNS/Neuroglial Endothelial 11. Endothelial cell (arteriole) 12. Endothelial cell (venule) 13. Endothelial cell (capillary) Stromal 14. Smooth muscle cell (arteriole) 15. Fibroblast 16. Ganglion cell (Meissner plexus) 17. Schwann cell Epithelial 1. Absorptive enterocyte 2. Goblet cell 3. Paneth cell 4. Crypt enterocyte 5. Enteroendocrine cell Endothelial 6. Endothelial cell (capillary or lymphatic) Stromal 7. Fibroblast Immune 8. Lymphocyte 9. Plasma cell 10. Eosinophil Epithelial Endothelial Stromal Immune
Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead 29 Lymph Node Spleen Muscle Diaphragm Aorta Duodenum Ileum Colon (Proximal) Colon (Distal) Bladder Trachea Lung (Proximal) Lung (Distal) 0 20 40 60 80 100 Relative compartment abundacy (%) Spatial Heterogeneity low medium high 10 20 30 40 50 60 70 Cell Type Representation, Pathology (%) 10 20 30 40 50 60 70 80 90 Cell Type Representation, Sequencing (%) Good correlation between functional compartments across modalities H&E + SEQUENCING PNS/Neuroglial Endothelial 11. Endothelial cell (arteriole) 12. Endothelial cell (venule) 13. Endothelial cell (capillary) Stromal 14. Smooth muscle cell (arteriole) 15. Fibroblast 16. Ganglion cell (Meissner plexus) 17. Schwann cell Epithelial 1. Absorptive enterocyte 2. Goblet cell 3. Paneth cell 4. Crypt enterocyte 5. Enteroendocrine cell Endothelial 6. Endothelial cell (capillary or lymphatic) Stromal 7. Fibroblast Immune 8. Lymphocyte 9. Plasma cell 10. Eosinophil Epithelial Endothelial Stromal Immune
BUILDING A MULTIMODAL REFERENCE ATLAS 30 Single cell & histology & microbiome Organ procurement Histopathology Single cell transcriptomics UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
BUILDING A MULTIMODAL REFERENCE ATLAS 30 Single cell & histology & microbiome Microbiome Organ procurement Histopathology Single cell transcriptomics UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C A Stomach Sigmoid colon Pyloric Sphincter Duodenum Jejunum Illeum Ascending colon 1 2 3 4 5 D E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Fig. 6 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C A Stomach Sigmoid colon Pyloric Sphincter Duodenum Jejunum Illeum Ascending colon 1 2 3 4 5 D E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Fig. 6 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) MUC Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C A Stomach Sigmoid colon Pyloric Sphincter Duodenum Jejunum Illeum Ascending colon 1 2 3 4 5 D E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Fig. 6 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) MUC large fraction of species was unique to each region Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C A Stomach Sigmoid colon Pyloric Sphincter Duodenum Jejunum Illeum Ascending colon 1 2 3 4 5 D E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Fig. 6 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) MUC 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon Jejunum Illeum Ascending colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Duodenum Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region F G -5 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) U MUC12 C M large fraction of species was unique to each region most species shared between ascending and sigmoid colon Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C A Stomach Sigmoid colon Pyloric Sphincter Duodenum Jejunum Illeum Ascending colon 1 2 3 4 5 D E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Fig. 6 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) MUC 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon Jejunum Illeum Ascending colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Duodenum Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region F G -5 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) U MUC12 C M Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 1 2 3 4 5 D E 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Bacteroidetes Actinobacteria Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 1 3 4 5 D E ending on 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Bacteroidetes Actinobacteria Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP large fraction of species was unique to each region most species shared between ascending and sigmoid colon Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
32 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead http://tabula-sapiens-portal.ds.czbiohub.org/
32 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead http://tabula-sapiens-portal.ds.czbiohub.org/
ACCELERATING SCIENTIFIC DISCOVERY 33 ~500k cells A compendium of human cell types across >20 donors Two methods for single cell transcriptomics FACS-based full length transcript analysis Micro fl uidic droplet-based 3’ end counting Histopathology images for most donors and organs >20TB >100k fi les ~200GB Data easily accessible from webportal https://tabula-sapiens-portal.ds.czbiohub.org/ Manuscript versions deposited to bioRxiv Code on GitHub https://github.com/czbiohub-sf/tabula-sapiens Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
34 TABULAE: WHOLE ORGANISM CELL ATLASES Species Reference State Tabula Muris Tabula Microcebus Tabula Drosophila Fly Cell Atlas Bat Cell Atlas Tabula Sapiens Disease/ Perturbation Intervention Nature, 2018 Nature, 2020 Nature, 2022 Science, 2022 biorxiv, 2020 biorxiv, 2021 Science, 2022 Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease Zebrahub Aging Fly Cell Atlas Science, 2023 biorxiv, 2022 Covid Tissue Atlas eLife, 2023 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
35 GROWING THE MULTIMODAL REFERENCE ATLAS The Tabula Sapiens Consortium, unpublished Tabula Sapiens v2 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
35 GROWING THE MULTIMODAL REFERENCE ATLAS T S P 1 T S P 2 T S P 3 T S P 4 T S P 5 T S P 6 T S P 7 T S P 8 T S P 9 T S P 1 0 T S P 1 1 T S P 1 2 T S P 1 3 T S P 1 4 T S P 1 5 T S P 1 7 T S P 1 9 T S P 2 0 T S P 2 1 T S P 2 5 T S P 2 6 T S P 2 7 T S P 2 8 T S P 3 0 Bladder Lung Muscle Blood Pancreas Heart Thymus Spleen Small_Intestine Skin Fat Kidney Trachea Lymph_Node Bone_Marrow Vasculature Large_Intestine Eye Uterus Tongue Mammary Liver Salivary_Gland Prostate Stomach Ear Testis Ovary 1,193,479 cells 28 tissues 24 donors 190 unique cell ontology classes 701 tissue-cell types The Tabula Sapiens Consortium, unpublished Tabula Sapiens v2 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
BUILDING A CLINICALLY RELEVANT MULTIMODAL ATLAS 36 Single cell transcriptomics Histology UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Single cell & histology & EMR The Tabula Sapiens Consortium, unpublished Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
BUILDING A CLINICALLY RELEVANT MULTIMODAL ATLAS 36 Single cell transcriptomics Histology UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Single cell & histology & EMR The Tabula Sapiens Consortium, unpublished Medical records Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
http://tabula-sapiens-portal.ds.czbiohub.org/ CZ Biohub Data Science CZ Biohub Genomics Ahmad Salehi Ravi Ponnusmi We express our gratitude and thanks to donor WEM and his family, as well as all of the anonymous organ and tissue donors and their families for giving both the gift of life and the gift of knowledge by their generous donations.

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  • 2. WHOLE ORGANISM CELL ATLASES 2 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 3. WHOLE ORGANISM CELL ATLASES 2 understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 4. WHOLE ORGANISM CELL ATLASES 2 Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 5. WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 6. WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + 1. Why single cells? Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 7. WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + Cells 1. Why single cells? Organ/Tissue Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 8. WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + Cells 1. Why single cells? Organ/Tissue Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 9. WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + Cells 1. Why single cells? Organ/Tissue Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 10. WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + Cells 1. Why single cells? 2. Why transcriptomics? Organ/Tissue Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 11. WHOLE ORGANISM CELL ATLASES 2 Single-cell transcriptomics Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease understand underlying mechanisms of disease and develop new technologies which will lead to actionable diagnostics and effective therapies Vision Computational analysis + In multicellular organisms, nearly every cell contains the same genome but not every gene is transcriptionally active in every cell Transcriptome, the total amount of RNA, offers a closer view of the real time gene expression in a cell Cells 1. Why single cells? 2. Why transcriptomics? Organ/Tissue Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 12. SINGLE CELL DATA COLLECTION WORKFLOW 3 The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 13. SINGLE CELL DATA COLLECTION WORKFLOW 3 Organ procurement The Tabula Sapiens Consortium, Science (2022) Partnership with Donor Network West Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 14. SINGLE CELL DATA COLLECTION WORKFLOW 3 Organ procurement Cell dissociation The Tabula Sapiens Consortium, Science (2022) Organ speci fi c teams Partnership with Donor Network West Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 15. SINGLE CELL DATA COLLECTION WORKFLOW 3 Organ procurement Cell dissociation 10x genomics Illumina sequencing Smartseq-2 Data analysis The Tabula Sapiens Consortium, Science (2022) Organ speci fi c teams Centralized Partnership with Donor Network West Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 16. SC-RNA SEQ DATA PROCESSING 4 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 17. SC-RNA SEQ DATA PROCESSING 4 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 18. SC-RNA SEQ DATA PROCESSING 4 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature UMAP1 UMAP2 Compartment endothelial epithelial germ line immune stromal UMAP1 UMAP2 Donor TSP1 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 TSP10 TSP11 TSP12 TSP13 TSP14 TSP15 10X smartseq2 UMAP1 UMAP2 Method 10X smartseq2 UMAP1 UMAP2 Gender female male Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 19. DATASET SUMMARY 5 Clinical metadata Age Gender Ethnicity Race BMI (kg/m2) Downtime (min) Cause of DeathTobacco (>20 pack-years) Alcohol IV Drug abuse Diabetes Cancer Hypertension Coronary Artery Disease Other noted conditions The Tabula Sapiens Consortium, Science (2022) TSP1 TSP2 TSP7 TSP14 Additional 11 Donors Bladder Blood Bone Marrow Eye Fat Heart Kidney Large Intestine Liver Lung Lymph Node Mammary Gland Muscle Pancreas Prostate Salivary Gland Skin Small Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Total 483,152 cells 24,583 50,115 12,297 10,650 20,263 11,505 9,641 13,680 5,007 35,682 53,275 11,375 30,746 13,497 16,375 27,199 9,424 12,467 34,004 33,664 15,020 9,522 7,124 16,037 6000 12000 18000 24000 30000 Cell count Number of cells Fig. 1 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 20. CELL TYPE ANNOTATION (I) 6 The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 21. CELL TYPE ANNOTATION (I) 6 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature
  • 22. CELL TYPE ANNOTATION (I) 6 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes Unannotated dataset Scanorama BBKNN scVI Random Forest SVM OnClass scANVI Unsupervised Method + kNN Supervised Method Semi-supervised Method PopularVote ANNOTATION Expert Manual Partial Annotation manually annotated dataset Cells Genes Expert Manual Annotation Revision computer automated annotated dataset Cells Genes annotated reference dataset Cells Genes The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature
  • 23. CELL TYPE ANNOTATION (II) 7 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 24. CELL TYPE ANNOTATION (II) 7 Reference Tabula Sapiens Draft reference Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 25. CELL TYPE ANNOTATION (II) 7 Reference *new* Tabula Sapiens subset Tabula Sapiens Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 26. CELL TYPE ANNOTATION (II) 7 Reference *new* Tabula Sapiens subset Tabula Sapiens Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 27. CELL TYPE ANNOTATION (II) 7 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 28. CELL TYPE ANNOTATION (II) 7 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Expert curation Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 29. CELL TYPE ANNOTATION (II) 8 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Expert curation Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 30. CELL TYPE ANNOTATION (II) 8 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Expert curation Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 31. 9 PopV (I) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 32. 9 PopV (I) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 33. 9 PopV (I) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 34. 9 PopV (I) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 35. 10 PopV (II) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 36. 10 PopV (II) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 37. 11 PopV (III) https://tabula-sapiens-portal.ds.czbiohub.org/annotateuserdata https://github.com/YosefLab/PopV Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 38. 11 PopV (III) https://tabula-sapiens-portal.ds.czbiohub.org/annotateuserdata https://github.com/YosefLab/PopV Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 39. 11 PopV (III) https://tabula-sapiens-portal.ds.czbiohub.org/annotateuserdata https://github.com/YosefLab/PopV Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 40. CELL TYPE ANNOTATION (III) 12 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Expert curation Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 41. CELL TYPE ANNOTATION (III) 12 Reference *new* Tabula Sapiens subset Tabula Sapiens *new* Tabula Sapiens Dataset Unclassified dataset Automatically classi fi ed dataset Expert curation Tabula Sapiens publication New Reference Draft reference Unclassified dataset Unclassi fi ed dataset Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 42. BUILDING A REFERENCE ATLAS 13 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes Unannotated dataset Scanorama BBKNN scVI Random Forest SVM OnClass scANVI Unsupervised Method + kNN Supervised Method Semi-supervised Method PopularVote ANNOTATION Expert Manual Partial Annotation manually annotated dataset Cells Genes Expert Manual Annotation Revision computer automated annotated dataset Cells Genes annotated reference dataset Cells Genes Automated annotations Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 43. BUILDING A REFERENCE ATLAS 13 10x SmartSeq2 raw count cell-gene matrix Cells Genes Ambient RNA Removal (DecontX) Dimensionality Reduction Harmonization (scVI) PREPROCESSING corrected counts cell-gene matrix Cells Genes unannotated dataset Cells Genes Unannotated dataset Scanorama BBKNN scVI Random Forest SVM OnClass scANVI Unsupervised Method + kNN Supervised Method Semi-supervised Method PopularVote ANNOTATION Expert Manual Partial Annotation manually annotated dataset Cells Genes Expert Manual Annotation Revision computer automated annotated dataset Cells Genes annotated reference dataset Cells Genes 475 cell types curation by tissue experts Automated annotations Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 44. 14 MARKER GENES FOR CELL TYPES Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 45. 14 MARKER GENES FOR CELL TYPES Wang, Pisco, et al, Nat Comms (2021) https://tabula-sapiens-portal.ds.czbiohub.org/markergenes Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 48. CROSS-TISSUE ANALYSIS 16 Macrophages Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 49. CROSS-TISSUE ANALYSIS n = 36,475 (broad) macrophage cell ontology class across 20 tissues 16 Macrophages Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 50. A L u n g S p l e e n L i v e r T r a c h e a T h y m u s E y e F a t P r o s t a t e H e a r t K i d n e y B l o o d L y m p h N o d e V a s c u l a t u r e S a l i v a r y G l a n d B o n e M a r r o w B l a d d e r M u s c l e S k i n M a m m a r y U t e r u s Lung Lymph Node Spleen Liver Trachea Thymus Vasculature Eye Fat Prostate Salivary Gland Heart Kidney Blood Bone Marrow Bladder Muscle Skin Mammary Uterus −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Pearson correlation Fig. S11 B Bladder Blood Bone_Marrow Eye Fat Heart Kidney Liver Lung Lymph_Node Mammary Muscle Prostate Salivary_Gland CROSS-TISSUE ANALYSIS n = 36,475 (broad) macrophage cell ontology class across 20 tissues 16 Macrophages Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 51. A L u n g S p l e e n L i v e r T r a c h e a T h y m u s E y e F a t P r o s t a t e H e a r t K i d n e y B l o o d L y m p h N o d e V a s c u l a t u r e S a l i v a r y G l a n d B o n e M a r r o w B l a d d e r M u s c l e S k i n M a m m a r y U t e r u s Lung Lymph Node Spleen Liver Trachea Thymus Vasculature Eye Fat Prostate Salivary Gland Heart Kidney Blood Bone Marrow Bladder Muscle Skin Mammary Uterus −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Pearson correlation Fig. S11 B Bladder Blood Bone_Marrow Eye Fat Heart Kidney Liver Lung Lymph_Node Mammary Muscle Prostate Salivary_Gland CROSS-TISSUE ANALYSIS n = 36,475 (broad) macrophage cell ontology class across 20 tissues 16 Macrophages r o w e r M u s c l e S k i n M a m m a r y U t e r u s Lung Lymph Node Spleen Liver Trachea Thymus Vasculature Eye Fat Prostate Salivary Gland Heart Kidney Blood Bone Marrow Bladder Muscle Skin Mammary Uterus .75 1.00 Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Legend for panels C (bottom) and D TSP1 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 TSP10 TSP11 TSP12 TSP13 TSP14 TSP15 Legend for panel C (top) D 0.0 0.1 0.2 Fat Skin Bone_Marrow Trachea Liver Eye Spleen Lung Kidney Lymph_Node Blood Salivary_Gland Tongue Tissue B 0 1 2 Median expression in group CHIT1 EREG CTSK CD5L Bladder Blood Bone_Marrow Eye Fat Heart Kidney Liver Lung Lymph_Node Mammary Muscle Prostate Salivary_Gland Skin Spleen Thymus Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 52. A L u n g S p l e e n L i v e r T r a c h e a T h y m u s E y e F a t P r o s t a t e H e a r t K i d n e y B l o o d L y m p h N o d e V a s c u l a t u r e S a l i v a r y G l a n d B o n e M a r r o w B l a d d e r M u s c l e S k i n M a m m a r y U t e r u s Lung Lymph Node Spleen Liver Trachea Thymus Vasculature Eye Fat Prostate Salivary Gland Heart Kidney Blood Bone Marrow Bladder Muscle Skin Mammary Uterus −1.00 −0.75 −0.50 −0.25 0.00 0.25 0.50 0.75 1.00 Pearson correlation Fig. S11 B Bladder Blood Bone_Marrow Eye Fat Heart Kidney Liver Lung Lymph_Node Mammary Muscle Prostate Salivary_Gland CROSS-TISSUE ANALYSIS n = 36,475 (broad) macrophage cell ontology class across 20 tissues 16 Macrophages r o w e r M u s c l e S k i n M a m m a r y U t e r u s Lung Lymph Node Spleen Liver Trachea Thymus Vasculature Eye Fat Prostate Salivary Gland Heart Kidney Blood Bone Marrow Bladder Muscle Skin Mammary Uterus .75 1.00 Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Legend for panels C (bottom) and D TSP1 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 TSP10 TSP11 TSP12 TSP13 TSP14 TSP15 Legend for panel C (top) D 0.0 0.1 0.2 Fat Skin Bone_Marrow Trachea Liver Eye Spleen Lung Kidney Lymph_Node Blood Salivary_Gland Tongue Tissue B 0 1 2 Median expression in group CHIT1 EREG CTSK CD5L Bladder Blood Bone_Marrow Eye Fat Heart Kidney Liver Lung Lymph_Node Mammary Muscle Prostate Salivary_Gland Skin Spleen Thymus Trachea Uterus Vasculature In macrophages, some genes are highly speci fi c to one tissue, others are speci fi c to multiple tissues, supporting the idea that tissue-resident macrophages are known to carry out specialized functions Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 55. CROSS-TISSUE ANALYSIS 18 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 56. CROSS-TISSUE ANALYSIS 18 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 57. CROSS-TISSUE ANALYSIS 19 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 58. CROSS-TISSUE ANALYSIS 19 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 59. CROSS-TISSUE ANALYSIS 20 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes IgA IgM Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 60. CROSS-TISSUE ANALYSIS 20 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes B cells undergo class-switch recombination to diversify the humoral immune response IgA IgM Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 61. CROSS-TISSUE ANALYSIS 20 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes Thymus Eye Prostate Tongue Fat Skin 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 log 2 cpm B 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Fraction of Cells Large_Intestine Bladder Mammary Small_Intestine Eye Skin Pancreas Vasculature Uterus Lung Salivary_Gland Liver Lymph_Node Kidney Spleen Bone_Marrow Fat Thymus Trachea Blood Tissue IgA IgG IgM/D B cells undergo class-switch recombination to diversify the humoral immune response IgA IgM Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 62. CROSS-TISSUE ANALYSIS 20 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes Thymus Eye Prostate Tongue Fat Skin 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 log 2 cpm B 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Fraction of Cells Large_Intestine Bladder Mammary Small_Intestine Eye Skin Pancreas Vasculature Uterus Lung Salivary_Gland Liver Lymph_Node Kidney Spleen Bone_Marrow Fat Thymus Trachea Blood Tissue IgA IgG IgM/D B cells undergo class-switch recombination to diversify the humoral immune response typically expressed in naïve B cells or secreted in the fi rst response to pathogens interact with pathogens and commensals at the mucosa involved in direct neutralization of pathogens IgA IgM Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 63. CROSS-TISSUE ANALYSIS 20 T cells ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 2 4 6 8 log 2 cpm B A C Large_Intestine Bladder Mammary Small_Intestine Eye Fig. 3 Tissue Clone id Cell ontology class B cells majority of T cell clones are found in multiple tissues and represent a variety of T cell subtypes Thymus Eye Prostate Tongue Fat Skin 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 log 2 cpm B 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Fraction of Cells Large_Intestine Bladder Mammary Small_Intestine Eye Skin Pancreas Vasculature Uterus Lung Salivary_Gland Liver Lymph_Node Kidney Spleen Bone_Marrow Fat Thymus Trachea Blood Tissue IgA IgG IgM/D B cells undergo class-switch recombination to diversify the humoral immune response typically expressed in naïve B cells or secreted in the fi rst response to pathogens interact with pathogens and commensals at the mucosa involved in direct neutralization of pathogens IgA IgM Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 64. 21 CROSS-TISSUE ANALYSIS Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature
  • 65. 21 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature
  • 66. 22 ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Thymus Eye Prostate Tongue Fat Skin Uterus Liver Pancreas Bladder Mammary Salivary_Gland Vasculature Lung Muscle Heart 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 Tissue log2 cpm C 0.5 0.6 0.7 0.8 ells IgA IgG IgM/D Cell ontology class ANKRD29 LCN1 ABCG2 KR ANKRD29 LC ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2 ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA O ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CY ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CY VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VI ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC ANKRD29 LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart LCN1 ABCG2 KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart KRT14 DSG2 F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart F2RL3 HILPDA OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart OIT3 FAM13C CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart CYTL1 PGF ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart ZG16B C7 VIPR1 CCL2 SLC14A1 Salivary_Gland Vasculature Lung Muscle Heart CROSS-TISSUE ANALYSIS Endothelial compartment Many of the endothelial markers are tissue-speci fi c while shared across donors Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 67. 23 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 68. 23 −10 −5 0 5 10 −5 0 5 10 UMAP 1 UMAP 2 Bladder Eye Fat Heart Liver Lung Mammary Muscle Pancreas Prostate Salivary_Gland Skin Thymus Tongue Uterus Vasculature Tissue −10 −5 0 5 10 −5 0 5 10 UMAP 1 TSP1 TSP10 TSP12 TSP14 TSP15 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 Donor UMAP 2 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 69. 23 −10 −5 0 5 10 −5 0 5 10 UMAP 1 UMAP 2 Bladder Eye Fat Heart Liver Lung Mammary Muscle Pancreas Prostate Salivary_Gland Skin Thymus Tongue Uterus Vasculature Tissue −10 −5 0 5 10 −5 0 5 10 UMAP 1 TSP1 TSP10 TSP12 TSP14 TSP15 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 Donor UMAP 2 −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 4 PLVAP −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 EDNRB Lung CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 70. 23 −10 −5 0 5 10 −5 0 5 10 UMAP 1 UMAP 2 Bladder Eye Fat Heart Liver Lung Mammary Muscle Pancreas Prostate Salivary_Gland Skin Thymus Tongue Uterus Vasculature Tissue −10 −5 0 5 10 −5 0 5 10 UMAP 1 TSP1 TSP10 TSP12 TSP14 TSP15 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 Donor UMAP 2 −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 4 PLVAP −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 EDNRB −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 4 MSX1 UMAP 2 −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 0 1 2 3 CYP1B1 Lung Muscle CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 71. 23 −10 −5 0 5 10 −5 0 5 10 UMAP 1 UMAP 2 Bladder Eye Fat Heart Liver Lung Mammary Muscle Pancreas Prostate Salivary_Gland Skin Thymus Tongue Uterus Vasculature Tissue −10 −5 0 5 10 −5 0 5 10 UMAP 1 TSP1 TSP10 TSP12 TSP14 TSP15 TSP2 TSP3 TSP4 TSP5 TSP6 TSP7 TSP8 TSP9 Donor UMAP 2 −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 4 PLVAP −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 EDNRB −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 UMAP 2 0 1 2 3 4 MSX1 UMAP 2 −10 −5 0 5 10 15 −5 0 5 10 UMAP 1 0 1 2 3 CYP1B1 Lung Muscle CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 72. 24 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead SLC14A1 appears to be a speci fi c marker for endothelial cells in the heart
  • 73. 24 Fig. S13 Heart muscle A B SLC14A1 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead SLC14A1 appears to be a speci fi c marker for endothelial cells in the heart
  • 74. 24 Fig. S13 Heart muscle A B SLC14A1 Heart muscle B SLC14A1 SLC14A1 CROSS-TISSUE ANALYSIS Endothelial compartment Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead SLC14A1 appears to be a speci fi c marker for endothelial cells in the heart Expression independently validated with data from the Human Protein Atlas!
  • 75. ALTERNATIVE SPLICING (I) 25 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
  • 76. ALTERNATIVE SPLICING (I) 25 annotated gene unannotated region A B C D E A: annotated junction B: Both boundaries are annotated but the splice is not C: One boundary is annotated and the other is not D: Neither boundary is annotated but at least one end is in a known gene E: Neither boundary is in a known gene Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Splice junctions are counted in categories based on annotation status Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
  • 77. ALTERNATIVE SPLICING (I) 25 annotated gene unannotated region A B C D E A: annotated junction B: Both boundaries are annotated but the splice is not C: One boundary is annotated and the other is not D: Neither boundary is annotated but at least one end is in a known gene E: Neither boundary is in a known gene Fraction of junctions in each category 22.8% 3.6% 12.5% 57.9% 3.2% 955,785 junctions total A - 217,855 B - 34,624 C - 119,276 D - 553,345 E - 30,685 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Splice junctions are counted in categories based on annotation status Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
  • 78. ALTERNATIVE SPLICING (I) 25 annotated gene unannotated region A B C D E A: annotated junction B: Both boundaries are annotated but the splice is not C: One boundary is annotated and the other is not D: Neither boundary is annotated but at least one end is in a known gene E: Neither boundary is in a known gene Fraction of junctions in each category 22.8% 3.6% 12.5% 57.9% 3.2% 955,785 junctions total A - 217,855 B - 34,624 C - 119,276 D - 553,345 E - 30,685 independent validation of 61% of the 358,924 total junctions catalogued in the RefSeq database! Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Splice junctions are counted in categories based on annotation status Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
  • 79. ALTERNATIVE SPLICING (I) 25 annotated gene unannotated region A B C D E A: annotated junction B: Both boundaries are annotated but the splice is not C: One boundary is annotated and the other is not D: Neither boundary is annotated but at least one end is in a known gene E: Neither boundary is in a known gene Fraction of junctions in each category 22.8% 3.6% 12.5% 57.9% 3.2% 955,785 junctions total A - 217,855 B - 34,624 C - 119,276 D - 553,345 E - 30,685 95.7% 3.0% Fraction of reads in each category 1,633,557,320 junctions total A - 1,562,568,353 B - 7,291,982 C - 13,361,370 D - 49,308,361 E - 1,027,254 independent validation of 61% of the 358,924 total junctions catalogued in the RefSeq database! Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Splice junctions are counted in categories based on annotation status Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
  • 80. ALTERNATIVE SPLICING (I) 25 annotated gene unannotated region A B C D E A: annotated junction B: Both boundaries are annotated but the splice is not C: One boundary is annotated and the other is not D: Neither boundary is annotated but at least one end is in a known gene E: Neither boundary is in a known gene Fraction of junctions in each category 22.8% 3.6% 12.5% 57.9% 3.2% 955,785 junctions total A - 217,855 B - 34,624 C - 119,276 D - 553,345 E - 30,685 95.7% 3.0% Fraction of reads in each category 1,633,557,320 junctions total A - 1,562,568,353 B - 7,291,982 C - 13,361,370 D - 49,308,361 E - 1,027,254 independent validation of 61% of the 358,924 total junctions catalogued in the RefSeq database! annotated junctions tend to be expressed at higher levels Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead Splice junctions are counted in categories based on annotation status Dehghannasiri, et al, Genome Biology (2021) Olivieri et al, Nat Methods (2022)
  • 81. ALTERNATIVE SPLICING (II) 26 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 82. ALTERNATIVE SPLICING (II) 26 4 5 exon #: 6 7 untranslated coding constitutive splice alternative splice MYL6 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 83. ALTERNATIVE SPLICING (II) 26 4 5 exon #: 6 7 untranslated coding constitutive splice alternative splice MYL6 -exon6 isoform previously described in phasic smooth muscle Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 84. ALTERNATIVE SPLICING (II) 26 4 5 exon #: 6 7 untranslated coding constitutive splice alternative splice MYL6 MYL6 splicing is differentially regulated across functionally different cell types like immune and endothelial -exon6 isoform previously described in phasic smooth muscle 0 1 e n d o t h e l i a l c e l l o f v a s c u l a r t r e e c a p i l l a r y e n d o t h e l i a l c e l l e n d o t h e l i a l c e l l o f a r t e r y v e i n e n d o t h e l i a l c e l l t y p e i i p n e u m o c y t e l u n g c i l i a t e d c e l l r e s p i r a t o r y g o b l e t c e l l b l a d d e r u r o t h e l i a l c e l l t y p e i p n e u m o c y t e m a c r o p h a g e n o n - c l a s s i c a l m o n o c y t e c l a s s i c a l m o n o c y t e m a c r o p h a g e m a c r o p h a g e s m o o t h m u s c l e c e l l p e r i c y t e c e l l f i b r o b l a s t p e r i c y t e c e l l m y o f i b r o b l a s t c e l l m e s e n c h y m a l s t e m c e l l t e n d o n c e l l s k e l e t a l m u s c l e s a t e l l i t e s t e m c e l l TSP1 0 1 Fraction including exon TSP2 0 1 TSP14 Bladder Lung Muscle p e i n o n - c l a s s i c c l a s s i c s m o o t h m y o f m e s e n c h y m s k e l e t a l m u s c l e s a t e Bladder Lung Muscle Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 85. ALTERNATIVE SPLICING (III) 27 D r e s p i r a t o r y g o b l e t c e l l b l a d d e r u r o t h e l i a l c e l l l u n g c TSP1 TSP2 TSP14 untranslated cytoplasmic transmembrane extracellular constitutive splice alternative splice 6 7 exon #: 9 10 8 11 CD47 C Epitheli Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 86. ALTERNATIVE SPLICING (III) 27 CD47 is another example of differential regulation of splicing for different cell types within an organ D r e s p i r a t o r y g o b l e t c e l l b l a d d e r u r o t h e l i a l c e l l l u n g c TSP1 TSP2 TSP14 untranslated cytoplasmic transmembrane extracellular constitutive splice alternative splice 6 7 exon #: 9 10 8 11 CD47 C Epitheli on 0 1 TSP14 D 7 8 9 10 r e s p i r a t o r y g o b l e t c e l l b l a d d e r u r o t h e l i a l c e l l l u n g c i l i a t e d c e l l t y p e i i p n e u m o c y t e c l u b c e l l t y p e i p n e u m o c y t e m a c r o p h a g e m a c r o p h a g e f i b r o b l a s t m e s e n c h y m a l s t e m c e l l TSP1 7 8 9 10 TSP2 7 8 9 10 TSP14 Average splice position 7 untranslated cytoplasmic transmembrane extracellular constitutive splice alternative splice 6 7 exon #: 9 10 8 11 CD47 C Endothelial Epithelial Immune Stromal Epithelial Immune Stromal e n d o t y p e i i p l u n g r e s p i r a t o r y b l a d d e r u r t y p e i p n m n o n - c l a s s i c a c l a s s i c a m m s m o o t h p p m y o f i b m e s e n c h y m a s k e l e t a l m u s c l e s a t e l l i Bladder Lung Muscle Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 87. BUILDING A MULTIMODAL DATASET (I) 28 Single cell & histology Organ procurement Single cell transcriptomics The Tabula Sapiens Consortium, Science (2022) UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 88. BUILDING A MULTIMODAL DATASET (I) 28 Single cell & histology Organ procurement Single cell transcriptomics Histopathology The Tabula Sapiens Consortium, Science (2022) UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 89. Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead 29 H&E + SEQUENCING PNS/Neuroglial Endothelial 11. Endothelial cell (arteriole) 12. Endothelial cell (venule) 13. Endothelial cell (capillary) Stromal 14. Smooth muscle cell (arteriole) 15. Fibroblast 16. Ganglion cell (Meissner plexus) 17. Schwann cell Epithelial 1. Absorptive enterocyte 2. Goblet cell 3. Paneth cell 4. Crypt enterocyte 5. Enteroendocrine cell Endothelial 6. Endothelial cell (capillary or lymphatic) Stromal 7. Fibroblast Immune 8. Lymphocyte 9. Plasma cell 10. Eosinophil
  • 90. Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead 29 Lymph Node Spleen Muscle Diaphragm Aorta Duodenum Ileum Colon (Proximal) Colon (Distal) Bladder Trachea Lung (Proximal) Lung (Distal) 0 20 40 60 80 100 Relative compartment abundacy (%) Spatial Heterogeneity low medium high H&E + SEQUENCING PNS/Neuroglial Endothelial 11. Endothelial cell (arteriole) 12. Endothelial cell (venule) 13. Endothelial cell (capillary) Stromal 14. Smooth muscle cell (arteriole) 15. Fibroblast 16. Ganglion cell (Meissner plexus) 17. Schwann cell Epithelial 1. Absorptive enterocyte 2. Goblet cell 3. Paneth cell 4. Crypt enterocyte 5. Enteroendocrine cell Endothelial 6. Endothelial cell (capillary or lymphatic) Stromal 7. Fibroblast Immune 8. Lymphocyte 9. Plasma cell 10. Eosinophil Epithelial Endothelial Stromal Immune
  • 91. Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead 29 Lymph Node Spleen Muscle Diaphragm Aorta Duodenum Ileum Colon (Proximal) Colon (Distal) Bladder Trachea Lung (Proximal) Lung (Distal) 0 20 40 60 80 100 Relative compartment abundacy (%) Spatial Heterogeneity low medium high 10 20 30 40 50 60 70 Cell Type Representation, Pathology (%) 10 20 30 40 50 60 70 80 90 Cell Type Representation, Sequencing (%) Good correlation between functional compartments across modalities H&E + SEQUENCING PNS/Neuroglial Endothelial 11. Endothelial cell (arteriole) 12. Endothelial cell (venule) 13. Endothelial cell (capillary) Stromal 14. Smooth muscle cell (arteriole) 15. Fibroblast 16. Ganglion cell (Meissner plexus) 17. Schwann cell Epithelial 1. Absorptive enterocyte 2. Goblet cell 3. Paneth cell 4. Crypt enterocyte 5. Enteroendocrine cell Endothelial 6. Endothelial cell (capillary or lymphatic) Stromal 7. Fibroblast Immune 8. Lymphocyte 9. Plasma cell 10. Eosinophil Epithelial Endothelial Stromal Immune
  • 92. BUILDING A MULTIMODAL REFERENCE ATLAS 30 Single cell & histology & microbiome Organ procurement Histopathology Single cell transcriptomics UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 93. BUILDING A MULTIMODAL REFERENCE ATLAS 30 Single cell & histology & microbiome Microbiome Organ procurement Histopathology Single cell transcriptomics UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature The Tabula Sapiens Consortium, Science (2022) Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 94. MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 95. MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C A Stomach Sigmoid colon Pyloric Sphincter Duodenum Jejunum Illeum Ascending colon 1 2 3 4 5 D E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Fig. 6 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 96. MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C A Stomach Sigmoid colon Pyloric Sphincter Duodenum Jejunum Illeum Ascending colon 1 2 3 4 5 D E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Fig. 6 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) MUC Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 97. MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C A Stomach Sigmoid colon Pyloric Sphincter Duodenum Jejunum Illeum Ascending colon 1 2 3 4 5 D E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Fig. 6 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) MUC large fraction of species was unique to each region Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 98. MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C A Stomach Sigmoid colon Pyloric Sphincter Duodenum Jejunum Illeum Ascending colon 1 2 3 4 5 D E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Fig. 6 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) MUC 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon Jejunum Illeum Ascending colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Duodenum Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region F G -5 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) U MUC12 C M large fraction of species was unique to each region most species shared between ascending and sigmoid colon Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 99. MICROBIOME COMPOSITION 31 Stomach Sigmoid colon Pyloric sphincter Duodenum Jejunum Illeum Ascending colon 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C A Stomach Sigmoid colon Pyloric Sphincter Duodenum Jejunum Illeum Ascending colon 1 2 3 4 5 D E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Fig. 6 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) MUC 0 25 50 75 100 Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 0 20 40 60 80 1 2 3 B C Sigmoid colon Jejunum Illeum Ascending colon 1 5 E 1: Duodenum 2: Jejunum 3: Ileum 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Duodenum Jejunum Ileum Ascending colon Sigmoid colon Bacteroidetes Actinobacteria Relative abundance (%) Observed species Shannon diversity ASVs specific to previous region ASVs specific to next region F G -5 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) U MUC12 C M Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 1 2 3 4 5 D E 4: Ascending colon 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Bacteroidetes Actinobacteria Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 0 2 4 6 8 10 12 14 16 18 20 -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP Bifidobacteriaceae Actinomycetaceae Rikenellaceae Prevotellaceae Bacteroidaceae Tannerellaceae Lachnospiraceae Eubacteraceae Veillonellaceae Lactobacillaceae Enterococcaceae Streptococcaceae Bacillaceae Ruminococcaceae Desulfovibrionaceae Burkholderiaceae Enterobacteriaceae Cardiobacteriaceae Akkermansiaceae Other 1 3 4 5 D E ending on 5: Sigmoid colon Verrucomicrobia Proteobacteria Firmicutes Bacteroidetes Actinobacteria Large intestine (sigmoid colon) Large intestine (ascending colon) Small intestine (ileum) Small intestine (duodenum) T cells CCR9 RPS6 MT-RNR1 MT-CO1 MT-CO2 MT-ND1 RPL30 IL7R RPS19 CXCR4 CCL4 GPR15 ANXA1 SELENBP1 C15orf48 FXYD3 KLRC2 CD24 GDF15 RARRES2 C10orf99 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 2 4 6 8 10 12 14 16 18 20 log2 (fold-change) -log 10 (p) Up-regulated in small intestine Up-regulated in large intestine MUC12 KLF4 CKB CRYAB FAM3D CFD MGP large fraction of species was unique to each region most species shared between ascending and sigmoid colon Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 100. 32 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead http://tabula-sapiens-portal.ds.czbiohub.org/
  • 101. 32 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead http://tabula-sapiens-portal.ds.czbiohub.org/
  • 102. ACCELERATING SCIENTIFIC DISCOVERY 33 ~500k cells A compendium of human cell types across >20 donors Two methods for single cell transcriptomics FACS-based full length transcript analysis Micro fl uidic droplet-based 3’ end counting Histopathology images for most donors and organs >20TB >100k fi les ~200GB Data easily accessible from webportal https://tabula-sapiens-portal.ds.czbiohub.org/ Manuscript versions deposited to bioRxiv Code on GitHub https://github.com/czbiohub-sf/tabula-sapiens Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 103. 34 TABULAE: WHOLE ORGANISM CELL ATLASES Species Reference State Tabula Muris Tabula Microcebus Tabula Drosophila Fly Cell Atlas Bat Cell Atlas Tabula Sapiens Disease/ Perturbation Intervention Nature, 2018 Nature, 2020 Nature, 2022 Science, 2022 biorxiv, 2020 biorxiv, 2021 Science, 2022 Understanding the differences from the normal state, inferring what went wrong and intervening to treat or manage Understanding disease Zebrahub Aging Fly Cell Atlas Science, 2023 biorxiv, 2022 Covid Tissue Atlas eLife, 2023 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 104. 35 GROWING THE MULTIMODAL REFERENCE ATLAS The Tabula Sapiens Consortium, unpublished Tabula Sapiens v2 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 105. 35 GROWING THE MULTIMODAL REFERENCE ATLAS T S P 1 T S P 2 T S P 3 T S P 4 T S P 5 T S P 6 T S P 7 T S P 8 T S P 9 T S P 1 0 T S P 1 1 T S P 1 2 T S P 1 3 T S P 1 4 T S P 1 5 T S P 1 7 T S P 1 9 T S P 2 0 T S P 2 1 T S P 2 5 T S P 2 6 T S P 2 7 T S P 2 8 T S P 3 0 Bladder Lung Muscle Blood Pancreas Heart Thymus Spleen Small_Intestine Skin Fat Kidney Trachea Lymph_Node Bone_Marrow Vasculature Large_Intestine Eye Uterus Tongue Mammary Liver Salivary_Gland Prostate Stomach Ear Testis Ovary 1,193,479 cells 28 tissues 24 donors 190 unique cell ontology classes 701 tissue-cell types The Tabula Sapiens Consortium, unpublished Tabula Sapiens v2 Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 106. BUILDING A CLINICALLY RELEVANT MULTIMODAL ATLAS 36 Single cell transcriptomics Histology UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Single cell & histology & EMR The Tabula Sapiens Consortium, unpublished Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 107. BUILDING A CLINICALLY RELEVANT MULTIMODAL ATLAS 36 Single cell transcriptomics Histology UMAP1 UMAP2 Organ/Tissue Bladder Blood Bone_Marrow Eye Fat Heart Kidney Large_Intestine Liver Lung Lymph_Node Mammary Muscle Pancreas Prostate Salivary_Gland Skin Small_Intestine Spleen Thymus Tongue Trachea Uterus Vasculature Single cell & histology & EMR The Tabula Sapiens Consortium, unpublished Medical records Data generation Cell type annotation Cross-tissue analysis Splicing Patterns Histology Microbiome Data sharing Looking ahead
  • 108. http://tabula-sapiens-portal.ds.czbiohub.org/ CZ Biohub Data Science CZ Biohub Genomics Ahmad Salehi Ravi Ponnusmi We express our gratitude and thanks to donor WEM and his family, as well as all of the anonymous organ and tissue donors and their families for giving both the gift of life and the gift of knowledge by their generous donations.