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

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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

Editor's Notes

  • Examples of questions in each part

    Intracellular:
    Assymetric division
    Local translation at synapses

    Tissue:
    Spatial organization of cells in tissue

  • Single-cell RNA-seq

    Spatially-resolved transcriptomic characterization such as by imaging

    Can reveal the spatial distribution as well as quantities of genes at a subcellular level as well as within individual, spatially-localized cells

    Help answer a wide range of biological questions

    Among questions I’m interested in:

    subcellular level: compartmental distributions of RNAs within cells provide an efficient way to produce proteins at the location of function and in response to local stimuli
    such as at synapses in neurons

    tissue level: role of spatial organization in development and function of normal tissues and in disease

    ability to perform spatially-resolved, single-cell transcriptomic profiling will provide important insight into many biological systems
  • Among techniques for spatially-resolved transcriptomic profiling

    The Zhuang lab pioneered multiplexed error-robust fluorescence in situ hybridization (MERFISH)

    enables…

    by introducing the strategy of using error-robust barcodes to encode individual RNA species

    imprinting the barcodes on RNAs using combinatorial oligonucleotide labeling

    and then reading out these barcodes through sequential rounds of imaging

    ---

    Show individual molecules
    Compare with sequential FISH -> N to N
    N imaging rounds allows you to identify 2^N
    By the time you get to so many rounds, errors will propagate -> so error correction is important
    Emphasize subset; bit flips can be corrected


  • Barcodes vary in length in order to encode more genes
    Barcode of length 16 or 16 bit
    2^16
    But error propagates
    Rather than using all possible barcodes, MERFISH uses a subset -> Hamming Distance 4
    This enables error detection and correction
  • For example, consider

    Consider a simple 3 bit barcode with 2 on bits (Hamming weight of 2, Hamming distance of 1)

    In each round, RNAs are imaged by FISH at the single-molecule level

    ----

    Change to one green box to focus on one

  • In the second round of hybridization, the same spot previously
  • Decode the barcode and assign the transcript identity
  • Reality, we have many more mRNAs that we typically encode using longer barcodes to accommodate more genes

    Use a subset of all possible codes to allow for error correction


    ADD "REAL DECODED DATA"

  • MERFISH has previously achieved single-cell RNA profiling in both cultured cells and brain tissues for 100 and 1000 genes with high efficiency
  • We recently increased the gene throughput of MERFISH, demonstrating simultaneous imaging of RNA transcripts of ~10,000 genes in single cells with ~80% detection efficiency and ~4% misidentification rate

    Subset of all possible
    16 bit barcode
    Hamming weight 4
    Hamming distance 4
    for error correction

    REPLACE WITH 10,000 GENE DATA?
  • Not just one cell
  • FIND HIGHER SPOT DENSITY IMAGE
  • Count each RNA species in each cell

    Enabling single-cell level transcriptome profiling while preserving the original spatial context of cells

    Substantially higher efficiency than single cell sequencing

    REMOVE SLIDE
  • In order to study the subcellular localization of RNAs, we combine MERFISH with immunostaining
  • Differential expression between the ER and non-ER cytoplasm compartments
    We can characterize the statistical enrichment of different genes at the ER
    Volcano plot

    The translation for mRNAs that encode secreted, glycosylated, and/or transmembrane proteins, collectively termed the secretome, has been shown to take place on the rough ER

    Indeed, we find that

    Gene set enrichment analysis to characterize which Gene Ontology terms are enriched, indeed

    We can successfully identify statistical enrichment

    ----

    Label dots -> show they are genes
    Emphasize comparison with other methods to identify gold-consensus
    MERFISH has high true positive, low false positive
  • Perhaps more compelling, we can visually see the spatial co-localization of these genes at the ER

    ---

    Identify potentially novel RNAs at ER -> Non-translation associated, spatial-localization independent of ribosomes

  • Likewise, we can do a similar analysis for the nucleus

    Confirm, a large number of lncRNAs and intron-retained isoforms are enriched in the nucleus

    Interestingly, although protein-coding mRNAs are translated in the cytoplasm, we identified 15% (579 protein-coding mRNAs) to be highly enriched in the nucleus

    We speculate that the retention of mRNAs in the nucleus may help buffer noise generated by stochastic mRNA production
    longer genes may take more time to be transcribed and exported may be enriched in the nucleus so that cells can quickly response to stimuli by exporting these RNAs to the cytoplasm to upregulate translation, bypassing the transcription step
  • Again, we can visually inspect these genes

    lincRNA MALAT1

    Highlight functions:
    Design probes to target particular isoforms
    Particular intron-retained isoform of EIF4A2 (ENST00000485101.5, Fig. S6C), which contains five different snoRNAs and one miRNA in its introns, and may thus regulate the expression of these small non-coding RNAs
  • We can quantify expression within the whole cell

    Cultured system
    transcriptional heterogeneity related to cell-cycle
  • Taking advantage of this spatial information, we observed that cells within each cluster tended to be spatially proximal to cells of the same cluster

    If we look at the composition of spatial neighbors versus non-neighbors, we see a significant enrichment

    Since this is a cultured system,
    We speculate that this phenomenon can be at least partly attributed to the fact that spatially neighboring cells are more likely sibling cells from the same mother cells, and hence tend to be in similar transcriptional clusters
  • Look for genes that exhibit spatial clustering or spatial heterogeneity in a manner that’s not associated with just cluster identifies

    ~300 such spatially highly heterogeneous genes
    ~300 such genes that did not show spatial heterogeneity in expression

    Examples include FGF18, a member of the fibroblast growth factor family of secreted proteins, and SMAD3, a member of the SMAD family of signal transducers and transcriptional modulators

    PKM, which is involved in glycolysis
    RPL36A, which encodes a ribosomal protein subunit
  • We reason that the spatial heterogeneity in the expression levels of these genes across cells might be due to local environmental stimulation or cell-cell communication

    We performed gene set enrichment analysis

    enrichment of terms associated with cellular response to growth factor and chemical stimuli among the spatially heterogenous genes but not the non-heterogeneous genes

    Even though these results were performed in a cultured system, I hope this illustrates the ability of spatially-resolved single-cell transcriptomics to characterize the interplay between transcriptional and spatial heterogeneity

    ---

    Show a few slides with mPOA, spatial heterogeneity?
  • Cells are fixed
    Providing a single static snapshot in time

    If we can estimate RNA velocity, the time derivative of the gene expression state, then we can infer the future transcriptional state of a cell on the timescale of hours

    greatly aid the analysis of developmental lineages and cellular dynamics

    ---
    What are specific questions?
    Pseudotime order of which cell develops into which cell, what types of question you can answer
  • Towards the end of my graduate training in the Kharchenko lab at Harvard Medical School

    We demonstrated inference of RNA velocity using scRNA-seq data using the ratio of unspliced vs. spliced mRNA

  • Simple model where…

    time derivative of each gene’s expression is a function of its input minus output
  • Under this model
    By observing the radio of spliced vs spliced mRNAs across a population of cells
    Expect steady state cells
    Whereas active induction or upregulation
    Active repression
    Relative to steady state
  • Perform regular clustering and dimensionality reduction
  • Predict the future transcriptional state
    Project that future cells onto the same embedding
  • Even without apriori knowledge

    We can see neuroprogenitor cells differentiating
    Other glial progenitors
    Begin to dive into the transcriptional programs regulating cell fate and differentiation at differentiation forks

    Spatial information is lost -> no idea where these cells are in tissue
    Preserving spatial information can show us how cells move spatially in development
    In cancer, point us to cell-type of origin
    Explore how cancer cells potentially migrate

    Highlight if we can see where these cells are in tissue
    then what answers we can answer if
    "post-docs want to know they're working on important questions”

  • Motivated us to develop an in situ analog

    Leverage the spatial information

    Again, assuming a simple model
  • Now by comparing the ratio of nucleus versus cytoplasmic mRNAs

    Find this model to be reasonable by looking at known cell cycle genes
    MCM6, mini-chromosome maintenance proteins required for initiation of eukaryotic genome replication, upregulated during G1/S
    KIF2C, kinesin-like protein that promotes mitotic chromosome segregation, upregulated during M
  • circle, interpret as a pseudotime ordering
  • We can systematically identify potentially novel cell-cycle-related genes
    regression model to identify genes with significant variance explained by pseudotime
    identified over 1600 genes with putative cell-cycle-dependent expression

    Stannin, a protein-coding gene with unknown cellular function


    Interestingly, by ordering genes based on their maximum points of expression along pseudotime
    gradual change of the transcription profile across cells along the pseudotime axis
    G1 phase, many genes were upregulated in succession

    These results suggest that a cell undergoes many gradual transcriptional changes as it progresses through some cell-cycle stages, rather than punctually transitioning from one cell-cycle stage to the next.


    ---

    Why is this part biologically interesting
    How many cell cycle related genes are known?
    Now identify 1600 that are potentially cell-cycle related
    States are not discrete

    Future: how to plan on using this technology


  • We envision that the ability of MERFISH, and other imaging-based methods, to provide quantitative and spatially-resolved RNA measurements at the genomic scale in single cells will allow a wide array of biological questions to be addressed.

    The subcellular resolution of image-based methods, combined with the ability to simultaneously image other cellular structures as we demonstrated here, enables the determination of the intracellular distribution and compartmentalization of RNAs and how this spatial organization changes as a function of cell states and in response to external stimuli.

    In addition, transcriptome imaging could also be combined with imaging of other molecular factors to probe, for example, chromatin structures and protein factors involved in transcriptional and post-transcriptional regulation.

    The quantitative single-cell expression profiling capability further allows distinct cell types and cell states to be identified and their spatial organizations to be determined in situ.

    This ability to spatially map transcriptionally distinct cells, in combination with in situ RNA velocity analysis powered by the discrimination of nuclear and cytoplasmic mRNAs, will further enhance our understandings of how the transcriptional properties and spatial patterns of cells evolve during differentiation, development, and disease progression.
  • People who are interested
    Glial development and gliomas

    Actively recruiting
  • Xiaowei Zhuang mentorship
    Chenglong Xia for developing and generating all the MERFISH data shown here
    George Emanuel for developing the MERFISH decoding software

    PhD mentor Peter Kharchenko
    discussions on the original RNA velocity theory

    Funding sources
    NCIs F99/K00 fellowship

    My lab website
    Contact info
    If you want to get in touch

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