"Lessons from working on the edge of human brain transcriptomics with spatially-resolved transcriptomics and deconvolution" seminar on 2023-05-23 at The Francis Crick Institute
@lcolladotor lcolladotor.github.io lcolladotor.github.io/bioc_team_ds Lessons from working on the edge of human brain transcriptomics with spatially-resolved transcriptomics and deconvolution Leonardo Collado Torres, Investigator The Francis Crick Institute May 23 2023 Slides available at speakerdeck.com/lcolladotor
Zoom in: snRNA-seq → deconvolution of bulk RNA-seq Matthew N Tran @mattntran Kristen R Maynard @kr_maynard Louise A Huuki-Myers @lahuuki Keri Martinowich @martinowk Stephanie C Hicks @stephaniehicks
Method Summary Method Regression Correction for Technical Variation Other Features MuSiC Wang et al, Nature Communications, 2019 W-NNLS regression (Weighted - Non-negative least squares) None Tree guided deconvolution, good for closely related cell types Bisque Jew et al, Nature Communications, 2020 NNLS regresion Gene specific transformation of bulk data Leverage overlapping bulk & sc data SCDC Dong et al, Briefings in Bioinformatics, 2020 W-NNLS framework proposed by MuSiC Option for Gene specific transformation of bulk data (from Bisque) Multiple reference datasets can be used, results combined with ENSEMBL weights DWLS Tsoucas, Nature Communications, 2019 Dampened Weighted least squares None 15
Method Regression Method Run Time Marker Evaluation Adjust for snRNA-seq vs. Bulk Tissues Tested Consider Cell Size Reference Set MuSiC W-NNLS Min. Internal Weighting No Pancreatic Islet, Rat & Mouse Kidney Yes Bisque NNLS Min. No Yes Adipose, DLPFC Recommend 3+ donors SCDC W-NNLS Min. Internal Weighting Yes Pancreatic Islet, mouse mammary Can input multiple references DWLS DWLS Hours Internal Selection No Mouse kidney, lung, liver, small intestine 16
Which Method is the Most Accurate? ● Benchmarking shows that different methods perform best on different data sets (Cobos et al, Nature Communications, 2020) ● Benchmarking results from different papers on “real” data ○ MuSiC paper: MuSiC > NNLS > BSEQ-sx > CIBERSORT ■ Pancreatic Islet: Beta cells vs. HbA1c (Fig 2a) ○ Bisque paper: Bisque > MuSiC > CIBERSORT ■ DLPFC: Microglia vs. Braak stage, Neuron vs. Cognitive diagnostic category (Fig 4) ○ SCDC paper: SCDC > MuSiC > Bisque > DWLS > CIBERSORT ■ Pancreatic Islet: Beta cells vs. HbA1c (Fig 4b) ○ Cobos benchmark: DWLS > MuSiC > Bisque > deconvoSeq ■ Human PMBC flow sorted (Fig 7) 17 Louise A Huuki-Myers @lahuuki
● Run with set of 20 & 25 marker genes per cell type ● Bisque is more robust to changes in the marker set than MuSiC Method Sensitivity to Marker Set 25 vs. 20 Genes Louise A Huuki-Myers @lahuuki
#deconvochallenge Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets doi.org/10.48550/arXiv.2305.06501 Sean Maden @MadenSean
Motivation ● Improve Deconvolution algorithms by considering differences in size and RNA content between cell types ● Use smFISH with RNAscope to establish data set of: ○ Cellular composition ○ Nuclei sizes of major cell types ○ Average nuclei RNA content of major cell types How do we measure total RNA content of a cell if we can only observe a few genes at a time? Use a TREG Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types research.libd.org/TREG/ doi.org/10.1101/2022.04.28.489923 Louise A Huuki-Myers @lahuuki #TREG
What is a TREG? ● Total RNA Expression Gene ● Expression is proportional to the overall RNA expression in a nucleus ● In smFISH the count of TREG puncta in a nucleus can estimate the RNA content Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types research.libd.org/TREG/ doi.org/10.1101/2022.04.28.489923 #TREG
Patterns of Observed Puncta ● TREGs were expressed in most cells ● AKT3 tracks really well with pattern of expression seen in snRNA-seq (ARID1B is also pretty good) snRNA-seq RNAscope Gene Mean Prop. Cells with Expression Prop. non-zero in DLPFC snRNA Standardized β (95% CI) AKT3 0.948 0.92 -1.38 (-1.39,-1.37) ARID1B 0.908 0.94 -0.62 (-0.62,-0.61) MALAT1 0.910 1.00 -0.11 (-0.12,-0.11) POLR2A 0.853 0.30 -0.98 (-0.99,-0.98) snRNA-seq NA NA -1.33 (-1.35,-1.31) Remember: MALAT1’s puncta data is unreliable research.libd.org/TREG/ doi.org/10.1101/2022.04.28.489923
#deconvochallenge Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets doi.org/10.48550/arXiv.2305.06501 Sean Maden @MadenSean
Zoom in: spatial omics Kristen R Maynard @kr_maynard Keri Martinowich @martinowk Stephanie C Hicks @stephaniehicks Andrew E Jaffe @andrewejaffe Stephanie C Page @CerceoPage
Visium Platform for Spatial Gene Expression Image from 10x Genomics - A slide contains 4 capture areas, each full of thousands of 55um-wide “spots” (often containing 1-10 cells) - Unique barcodes in each spot bind to particular genes; after sequencing, gene expression can be tied back to exact spots, forming a spatial map Kristen R. Maynard 33
bioconductor.org/packages/spatialLIBD Pardo et al, 2022 DOI 10.1186/s12864-022-08601-w Maynard, Collado-Torres, 2021 DOI 10.1038/s41593-020-00787-0 Brenda Pardo Abby Spangler @PardoBree @abspangler Louise A. Huuki-Myers @lahuuki
DOI: 10.1038/s41593-020-00787-0 twitter.com/lcolladotor/status/1233661576433061888 from 2020-02-29 Andrew E Jaffe @andrewejaffe Kristen R Maynard @kr_maynard Keri Martinowich @martinowk
DOI: 10.1038/s41593-020-00787-0 twitter.com/lcolladotor/status/1233661576433061888 from 2020-02-29 DOI 10.1093/bioinformatics/btac299 Since Feb 2020 spatialLIBD::fetch_data() provides access to SpatialExperiment R/Bioconductor objects Stephanie C Hicks @stephaniehicks Lukas M Weber @lmweber
DOI: 10.1038/s41593-020-00787-0 twitter.com/lcolladotor/status/1233661576433061888 from 2020-02-29 twitter.com/CrowellHL/status/1597579271945715717 DOI 10.1093/bioinformatics/btac299 Since Feb 2020 spatialLIBD::fetch_data() provides access to SpatialExperiment R/Bioconductor objects
BayesSpace clustering with batch correction worked best for multiple samples 41 doi.org/10.1101/2023.02.15.528722 twitter.com/CrowellHL/status/1597579271945715717
Different Resolutions of BayesSpace Clustering k = number of clusters ● k=2: separate white vs. grey matter ● k=9: best reiterated histological layers ● k=16: data-driven optimal k based on fast H+ statistic 42 More Clusters = More Complexity doi.org/10.1101/2023.02.15.528722
Spatial Registration Adds Anatomical Context ● Validate detection of laminar structure ● Correlate enrichment t-statistics for top marker genes of reference ○ Cluster vs. manual annotation ● Annotate with strongly associated histological layer 43 Sp k D d ~L doi.org/10.1101/2023.02.15.528722
Existing Spot Deconvolution Software - Explored 3 novel software methods from the literature Software name Overall approach Input Cell Counts Output Tangram (Biancalani et al.) Mapping individual cells Every spot Integer counts Cell2location (Kleshchevnikov et al.) Matching gene-expression profile Average across spots Decimal counts SPOTlight (Elosua-Bayes et al.) Matching gene-expression profile Not used Proportions 48 Excit L5 Counts
Benchmarking Spot Deconvolution Software: Theory - How do we measure performance or accuracy of cell-type predictions? - Make orthogonal measurements*: image-derived counts - Leverage prior knowledge: neurons localize to gray matter? - Self-consistency of results: broad vs. fine cell-type results 49 Nicholas J Eagles @Nick-Eagles (GitHub) doi.org/10.1101/2023.02.15.528722
Visium-SPG for Paired Imaging and Gene Expression - Channels measure proteins marking for broad cell types: microglia, neurons, oligodendrocytes, and astrocytes Kristen R. Maynard doi.org/10.1101/2023.02.15.528722
Using IF to Quantify Cell Types - Visium-SPG IF images mark for several proteins - Fluorescence in image channels correlates with counts of measured cell types Can measure 5 distinct cell types: 53 ● Astrocyte (GFAP) ● Neuron (NeuN) ● Oligodendrocyte (OLIG2) ● Microglia (TMEM119) ● Other (low signal in all channels) doi.org/10.1101/2023.01.28.525943 Sriworarat, 2023. samuibrowser.com Sang Ho Kwon @sanghokwon17
Segmenting Cells on Visium-SPG IF Images 1. Segment cells on IF image 2. Manually label N cells 3. Train cell-type classifier and apply on remaining data Sriworarat, 2023. samuibrowser.com 54 Nicholas J Eagles @Nick-Eagles (GitHub) doi.org/10.1101/2023.01.28.525943
Constructing Dataset of Labeled Cells 1. Segment cells on IF image 2. Manually label example cells 3. Train cell-type classifier and apply on remaining data Image Channels Classified Cell Type Cell Mask 55 Annie B. Nguyen
Addressing Bias in Cell Selection - Trained logistic regression model on 600-cell dataset - Broke cells into 4 quartiles based on model confidence - Labelled 320 more cells, evenly sampled from all 4 quartiles 57 Cell Type Probability Astro 0.2 Oligo 0.3 Micro 0.1 Neuron 0.45 Other 0.05 4 quartiles * 4 sections * 5 cell types * 4 cells = 320 new cells total 600 old cells + 320 new cells = 920 labeled cells Nicholas J Eagles @Nick-Eagles (GitHub)
Training Cell-Type Classifier 58 Model Test Precision Test Recall Decision tree 0.86 0.87 Dataset # Training # Test Split Old 600 480 120 80/20 New 320 240 80 75/25 Combined 920 720 200 ~78/22 1. Segment cells on IF image 2. Manually label N cells 3. Train cell-type classifier and apply on remaining data Grid search with 5-fold CV for each model to select hyperparameters Data Model Final model chosen
Layer-level: collapse Excit_L* into 1 for broad-level 60 Then combine Excitatory and Inhibitory into Neurons to compare vs CART-calculated counts from Visium_SPG
Benchmark Results: Leverage Prior Knowledge - Manually annotate spots with histological layer - Explore how cell-type predictions map to annotated layers Kristen R. Maynard A B C
Benchmark Results: Self-Consistency of Results Counts from software results using both broad and layer-level cell types were compared, by “collapsing” onto just 4 major cell types. We expect results perfectly on the diagonal! 63
How Spot Deconvolution Results Were Used A. Better characterize unsupervised spatial domains B. Cell-cell communication; cell-type-informed ligand-receptor interactions in the context of schizophrenia risk A 66 Boyi Guo Melissa Grant-Peters
Having more data is useful to provide context! Here 4 new samples have low sequencing saturation (outliers) but are within range of good samples from other studies
Software keeps evolving and as leaders in the field we aim to use the best methods 78 Moses, L., Pachter, L. Museum of spatial transcriptomics. Nat Methods 19, 534–546 (2022). https://doi.org/10.1038/s41592-022-01409-2
The Development Process - Making a module - New, experimental software can change dramatically (function and syntax) between versions - Promotes collaboration by allowing two researchers to share exact code and instantly run software without special set-up SpatialExperiment release 3.14 SpatialExperiment devel 3.15 module load tangram/1.0.2 module load cell2location/0.8a0 module load spagcn/1.2.0 https://github.com/LieberInstitute/jhpce_mod_source https://github.com/LieberInstitute/jhpce_module_config Nicholas J Eagles @Nick-Eagles (GitHub)
The Development Process - Regular interaction with software authors to clarify functionality and report bugs - Documentation for code and author responsiveness on GitHub can be critical in successfully applying software to our data Nicholas J Eagles @Nick-Eagles (GitHub)
More challenges ahead Working with multiple capture areas per tissue Nicholas J Eagles @Nick-Eagles (GitHub) Prashanthi Ravichandran @prashanthi-ravichandran (GH) Spot diameter error: ~1.8 → ~1.1 Another pair: ~2.8 → ~0.76
lcolladotor.github.io/#projects ● Every assay has caveats ● We re-use tricks: think adding 0, multiplying by 1 ● It nearly always takes a team ● Data sharing accelerates science + democratizes access to it ● Zooming in allows us to reduce the heterogeneity ● We can learn from each other: from uniformly processing our data & re-using it → replicate / validate?
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@MadhaviTippani Madhavi Tippani @HeenaDivecha Heena R Divecha @lmwebr Lukas M Weber @stephaniehicks Stephanie C Hicks @abspangler Abby Spangler @martinowk Keri Martinowich @CerceoPage Stephanie C Page @kr_maynard Kristen R Maynard @lcolladotor Leonardo Collado-Torres @Nick-Eagles (GH) Nicholas J Eagles Kelsey D Montgomery Sang Ho Kwon Image Analysis Expression Analysis Data Generation Thomas M Hyde @lahuuki Louise A Huuki-Myers @BoyiGuo Boyi Guo @mattntran Matthew N Tran @sowmyapartybun Sowmya Parthiban Slides available at speakerdeck.com /lcolladotor + Many more LIBD, JHU, and external collaborators @mgrantpeters Melissa Grant-Peters @prashanthi-ravichandran (GH) Prashanthi Ravichandran
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