29 Expanding the resolution of gene expression analyses: spatially (spatialLIBD) and in numbers (recount3) Leonardo Collado-Torres, Ph.D., Investigator Lieber Institute for Brain Development CDC/ATSDR R User Group 2021-01-28 @lcolladotor @LieberInstitute #recount3 #spatialLIBD
Overview 6 1. Identification of layer-enriched genes in human cortex using Visium. 2. Spatial registration of single-nucleus RNA-seq data from human cortex. 3. Layer-enriched expression of genes associated with brain disorders. Maynard, Collado-Torres, et al, bioRxiv, 2020 @kr_maynard
Study design for Visium experiments in dorsolateral prefrontal cortex (DLPFC) 7 Andrew E Jaffe Keri Martinowich Stephanie C Hicks Lukas M Weber Cedric Uytingco Nikhil Rao @stephaniehicks @lmwebr @martinowk @andrewejaffe
Three statistical models to assess laminar enrichment “ANOVA” model 11 “Enrichment” model “Pairwise” model Maynard, Collado-Torres, et al, bioRxiv, 2020 Is any layer different? Is one layer > the rest? Is layer X > layer Y?
12 ISH images courtesy of Allen Human Brain Atlas: http://human.brain-map.org/ (Hawrylycz et al., 2012) Maynard, Collado-Torres, et al, bioRxiv, 2020 Visium replicates layer-enrichment of previously identified layer marker genes L4>rest, p=1.74e-09 L6>WM, p=4.48e-19 logcounts logcounts
L4 L3 L2 L1 0.0 0.2 0.4 0.6 0.8 (A) (B) (C) Maynard, Collado-Torres, et al, bioRxiv, 2020 Spatial registration of your sc/snRNA-seq data Your sc/snRNA-seq data Hodge et al, Nature, 2019
L4 L3 L2 L1 0.0 0.2 0.4 0.6 0.8 (A) (B) (C) Maynard, Collado-Torres, et al, bioRxiv, 2020 Spatial registration of your sc/snRNA-seq data Your sc/snRNA-seq data Our spatial data Hodge et al, Nature, 2019
16 Maynard, Collado-Torres, et al, bioRxiv, 2020 12 15 Matthew N Tran Brianna K Barry @mattntran Identify clusters in your sc/snRNA-seq data - Pre-process your sc/snRNA-seq data - Identify cell/nuclei clusters - Find data-driven marker genes and/or combine with known marker genes - Label clusters
18 Maynard, Collado-Torres, et al, bioRxiv, 2020 Your sc/snRNA-seq: cell or nuclei clusters * subjects or other analysis variables “Pseudo-bulk” your sc/snRNA-seq data
Three statistical models to assess laminar enrichment “ANOVA” model 19 “Enrichment” model “Pairwise” model Is any layer different? Is one layer > the rest? Is layer X > layer Y? Maynard, Collado-Torres, et al, bioRxiv, 2020
23 Maynard, Collado-Torres, et al, bioRxiv, 2020 Cluster1 Cluster2 Cluster3 ENSG00000104419 3 -2 0.3 ENSG0000018400 7 1 0.67 4 … … … … Full example table https://github.com/LieberInstitute/spatialLIBD/blob/master/data-raw/tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer.csv Save your “enrichment” t- statistics for your sc/snRNA-seq clusters Spatial registration of your sc/snRNA-seq data: DIY
27 Stephanie C Hicks Lukas M Weber @stephaniehicks @lmwebr Maynard, Collado-Torres, et al, bioRxiv, 2020 Data-driven layer-enriched clustering in the DLPFC Spatially-varying genes Highly-variable genes Spot-level clustering Manual layer annotation using spatialLIBD • Which samples to use? • All samples? • Sample by sample then merge? • Use image-derived information?
28 Maynard, Collado-Torres, et al, bioRxiv, 2020 Data-driven layer-enriched clustering in the DLPFC SpatialDE by Svensson et al, Nature Methods, 2018 Are the spatial patterns relevant? Remember to inspect your data!
29 Maynard, Collado-Torres, et al, bioRxiv, 2020 Data-driven layer-enriched clustering in the DLPFC SpatialDE by Svensson et al, Nature Methods, 2018 “ANOVA” model F-statistics SpatialDE statistic
30 Maynard, Collado-Torres, et al, bioRxiv, 2020 Use known marker genes only Use layer- enriched genes (scenario where you have more datasets) Only use the data Requires >=1 expert Benefits from known marker genes (if expressed) & prior knowledge
Summary: transcriptome-scale spatial gene expression in postmortem human cortex 33 http://research.libd.org/spatialLIBD Explore the data: Maynard, Collado-Torres, et al, bioRxiv, 2020
Acknowledgements Lieber Institute Keri Martinowich Andrew E. Jaffe Brianna K. Barry Joseph L. Catallini II Matthew N. Tran Zachary Besich Madhavi Tippani Joel E. Kleinman Thomas M. Hyde Daniel R. Weinberger JHU Biostatics Dept JHU Oncology Tissue Services (Kristen Lecksell) Stephanie C. Hicks JHU SKCCC Flow Core (Jessica Gucwa) Lukas M. Weber JHU Transcriptomics & Deep Sequencing Core (Linda Orzolek) 10x Genomics Cedric Uytingco Stephen R. Williams Jennifer Chew Yifeng Yin Nikhil Rao 43 @kr_maynard @lcolladotor #spatialLIBD Interested in working with us? Let us know!
expression data for ~700,000 human samples (multiple) positions available This project involves the Hansen, Langmead, Leek and Battle labs at JHU & the Nellore lab at OHSU & the Collado-Torres lab at LIBD Contact: • Kasper D. Hansen www.hansenlab.org • Ben Langmead www.langmead-lab.org/ • Leonardo Collado-Torres lcolladotor.github.io/ • Abhinav Nellore nellore.bio/ • Alexis Battle battlelab.jhu.edu/ • Jeff Leek jtleek.com/ • Andrew Jaffe aejaffe.com/ @chrisnwilks #recount3
lcolladotor
s1ok69oo
yayaya
trycycle
genomethica
ailaboocu
mkkon
ykamit
koro485
signer
keyakkie
higgi13425
thomaselove
thoeni
moore
shlominoach
bryan
chriscoyier
destraynor
philhawksworth
paulrobertlloyd
jensimmons
brad_frost
holman
keavy