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Spatial transcriptome profiling by MERFISH reveals sub-cellular RNA compartmentalization and cell-cycle dependent gene expression

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For KOGO/SGI South Korea 2019

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Spatial transcriptome profiling by MERFISH reveals sub-cellular RNA compartmentalization and cell-cycle dependent gene expression

  1. 1. Spatial transcriptome profiling by MERFISH reveals sub-cellular RNA compartmentalization and cell-cycle dependent gene expression Jean Fan, PhD NCI F99/K00 Fellow Zhuang Lab Harvard University
  2. 2. Spatially-resolved transcriptomic characterization by imaging can reveal the spatial distribution and quantities of genes at subcellular and cellular levels Jean Fan, PhD | SGI | September 2019 2 Neuronal-glial interaction in development (Perea et al. Front Cell Neurosci. 2014.) Tumor-microenvironment interaction in cancer (Guha. The Pharmaceutical Journal. 2014.) cellular/tissue levelsubcellular level (Glock et al, Current Opinions in Neurobiology 2017)
  3. 3. smFISH enables spatially-resolved RNA measurements at the single-cell and subcellular resolution but for very few targets due to spectral overlap Jean Fan, PhD | SGI | September 2019 3 (Source: BioCat GmbH)
  4. 4. Multiplexed error-robust FISH (MERFISH) uses an error-robust barcoding scheme to enable simultaneous transcriptome-scale measurements Jean Fan, PhD | SGI | September 2019 (Chen et al, Science 2015) 4
  5. 5. Multiplexed error-robust FISH (MERFISH) uses an error-robust barcoding scheme to enable simultaneous transcriptome-scale measurements Jean Fan, PhD | SGI | September 2019 5 (Chen et al, Science 2015)
  6. 6. MERFISH uses error-robust binary codes to enable error detection and correction Jean Fan, PhD | SGI | September 2019 6
  7. 7. Jean Fan, PhD | SGI | September 2019 7 Simulated Raw Data Image 1 Decoded barcode: 1 3 bit barcode … Gene X : 101 Gene Y : 011 …
  8. 8. Jean Fan, PhD | SGI | September 2019 8 Simulated Raw Data Image 2 Decoded barcode: 10 3 bit barcode … Gene X : 101 Gene Y : 011 …
  9. 9. Jean Fan, PhD | SGI | September 2019 9 Simulated Raw Data Image 3 Decoded barcode: 101 3 bit barcode … Gene X : 101 Gene Y : 011 …
  10. 10. Jean Fan, PhD | SGI | September 2019 10 Simulated Raw Data Image 3 Decoded barcode: 101 3 bit barcode … Gene X : 101 Gene Y : 011 … Gene X
  11. 11. Jean Fan, PhD | SGI | September 2019 11 Decoded Data
  12. 12. Jean Fan, PhD | SGI | September 2019 12 Decoded Data
  13. 13. Jean Fan, PhD | SGI | September 2019 13 Decoded Data (Chen et al, Science 2015) (Wang et al, Sci Rep 2018)
  14. 14. Jean Fan, PhD | SGI | September 2019 14 Decoded Data ~80% detection efficiency ~4% mis- identification
  15. 15. Jean Fan, PhD | SGI | September 2019 15
  16. 16. Jean Fan, PhD | SGI | September 2019 16 100s to 100,000s of cells within fixed cultures and tissues
  17. 17. Jean Fan, PhD | SGI | September 2019 17 Segment cells and count mRNAs
  18. 18. Jean Fan, PhD | SGI | September 2019 18 a React WebGL Tool for Exploring Spatially Resolved Single-Cell Transcriptomics Data
  19. 19. Such high-throughput spatially-resolved measurements combined with novel computational analyses enable addressing many biological questions Jean Fan, PhD | SGI | September 2019 Subcellular localization of RNAs Spatial organization of single-cell clusters 19
  20. 20. Such high-throughput spatially-resolved measurements combined with novel computational analyses enable addressing many biological questions Jean Fan, PhD | SGI | September 2019 20 Subcellular localization of RNAs Spatial organization of single-cell clusters
  21. 21. MERFISH + immunostaining + segmentation identifies genes statistically enriched at different subcellular compartments Jean Fan, PhD | SGI | September 2019 KDEL staining for ER DAPI staining for nucleus 21
  22. 22. MERFISH + KDEL staining + segmentation confirms co- localization of secretome genes to the ER Jean Fan, PhD | SGI | September 2019 22
  23. 23. Jean Fan, PhD | SGI | September 2019 23
  24. 24. MERFISH + DAPI staining + segmentation identifies genes enriched in the nucleus Jean Fan, PhD | SGI | September 2019 24
  25. 25. Jean Fan, PhD | SGI | September 2019 25
  26. 26. Such high-throughput spatially-resolved measurements combined with novel computational analyses enable addressing many biological questions Jean Fan, PhD | SGI | September 2019 26 Subcellular localization of RNAs Spatial organization of single-cell clusters
  27. 27. Just like in scRNA-seq analysis, single-cell clustering analysis identifies transcriptionally-distinct clusters Jean Fan, PhD | SGI | September 2019 27
  28. 28. Spatial information reveals cells within each transcriptionally- distinct cluster tend to be spatially proximal to cells of the same cluster Jean Fan, PhD | SGI | September 2019 28
  29. 29. Spatially heterogeneous genes can be identified using Moran’s I, a measurement of spatial clustering Jean Fan, PhD | SGI | September 2019 Spatially heterogeneous Spatially non-heterogeneous 29
  30. 30. Jean Fan, PhD | SGI | September 2019 Spatially heterogeneous genes tend to be enriched in cellular communication processes 30
  31. 31. MERFISH + computational modeling enables derivation of RNA velocity in situ Jean Fan, PhD | SGI | September 2019 31
  32. 32. Background: Kharchenko and Linnarson lab previously demonstrated inference of RNA velocity using scRNA-seq data using the ratio of unspliced vs. spliced mRNA Jean Fan, PhD | SGI | September 2019 32
  33. 33. Background: Kharchenko and Linnarson lab previously demonstrated inference of RNA velocity using scRNA-seq data using the ratio of unspliced vs. spliced mRNA Jean Fan, PhD | SGI | September 2019 33
  34. 34. Background: Kharchenko and Linnarson lab previously demonstrated inference of RNA velocity using scRNA-seq data using the ratio of unspliced vs. spliced mRNA Jean Fan, PhD | SGI | September 2019 34
  35. 35. Background: Kharchenko and Linnarson lab previously demonstrated inference of RNA velocity using scRNA-seq data using the ratio of unspliced vs. spliced mRNA Jean Fan, PhD | SGI | September 2019 35
  36. 36. Background: Kharchenko and Linnarson lab previously demonstrated inference of RNA velocity using scRNA-seq data using the ratio of unspliced vs. spliced mRNA Jean Fan, PhD | SGI | September 2019 36
  37. 37. Jean Fan, PhD | SGI | September 2019 37
  38. 38. Adapt RNA velocity model for in situ analog using ratio of nuclear vs. cytoplasmic mRNAs Jean Fan, PhD | SGI | September 2019 38
  39. 39. Jean Fan, PhD | SGI | September 2019 39
  40. 40. Jean Fan, PhD | SGI | September 2019 At steady state: Future transcriptional state: 40
  41. 41. RNA velocity predicts future transcriptional state of cell to place cells in pseudotime Jean Fan, PhD | SGI | September 2019 41
  42. 42. RNA velocity predicts future transcriptional state of cell to place cells in pseudotime Jean Fan, PhD | SGI | September 2019 42
  43. 43. Pseudotime analysis identifies putative cell-cycle genes and highlights gradual transcriptional change throughout select cell-cycle stages Jean Fan, PhD | SGI | September 2019 43
  44. 44. Summary ◦ MERFISH enables simultaneous spatial transcriptomic profiling for 10k genes with high efficiency and accuracy ◦ Combined with immunostaining identifies genes spatially co- localized at subcellular compartments ◦ Segmented into single-cells enables spatially-resolved transcriptomic profiling ◦ RNA velocity can be inferred in situ by discriminating nuclear and cytoplasmic mRNAs to predict future transcriptional cell state Jean Fan, PhD | SGI | September 2019 44
  45. 45. Jean Fan, PhD | SGI | September 2019 45
  46. 46. Thanks and happy to take questions! Zhuang Lab Find me online! Web: http://JEF.works Email: jeanfan@fas.harvard.edu Twitter: @JEFworks Funding Xiaowei Zhuang Chenglong Xia George Emanuel George Hao Alec Goodman Ryan Hanna Kharchenko Lab Peter Kharchenko Ruslan Soldatov

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