TREG_poster

Data-driven Identification of Total RNA Expression Genes (TREGs) for
Estimation of RNA Abundance in Heterogeneous Cell Types

Louise Huuki Myers Biology of Genomes 22 Poster

Transcript

1. Data-driven Identification of Total RNA Expression Genes (TREGs) for
   Estimation of RNA Abundance in Heterogeneous Cell Types
   L.A. Huuki-Myers1, K.D. Montgomery1, S.H. Kwon1,2, S.C. Page1, S.C. Hicks3, K.R. Maynard1,4*, L. Collado-Torres1,5*
   1. Lieber Institute for Brain Development, 2. Department of Neuroscience Johns Hopkins School of Medicine, 3. Department of Biostatistics Johns Hopkins Bloomberg
   School of Public Health, 4. Department of Psychiatry and Behavioral Sciences JHSOM, 5. Center for Computational Biology Johns Hopkins University
   BAPTA
   Abstract
   Conclusion
   Acknowledgements
   Validation of TREGs with smFISH +
   RNAscope
   Presenter &
   Poster requests:
   Louise.Huuki@libd.org
   @lahuuki Download this Poster:
   BioRxiv Pre-print
   TODO
   Next-generation sequencing technologies have facilitated data-driven
   identification of gene sets with different features including genes with
   stable expression, cell-type specific expression, or spatially variable
   expression. Here, we aimed to define and identify a new class of
   "control" genes called) Total RNA Expression Genes (TREGs), which
   correlate with total RNA abundance in heterogeneous cell types of
   different sizes and transcriptional activity. We provide a data-driven
   method to identify TREGs from single cell RNA-sequencing (RNA-seq)
   data, available as an R/Bioconductor package. We demonstrated the
   utility of our method in the postmortem human brain using multiplex
   single molecule fluorescent in situ hybridization (smFISH) and
   compared candidate TREGs against classic housekeeping genes. We
   identified AKT3 as a top TREG across five brain regions, especially in the
   dorsolateral prefrontal cortex.
   Rank Invariance Calculation
   Software tools available as R/BioC package:
   bioconductor.org/packages/TREG
   i. Filter for low expressed genes
   ii. Compute Expression Rank of each
   nucleus for each gene
   iii. Calculate mean gene expression across
   all nuclei for one cell type and then its
   Rank Expression.
   iv. Per gene, find the difference of the
   Rank Expression against the mean Rank
   Expression for each nucleus in a given
   cell type.
   v. Calculate the mean of the absolute
   Expression Rank differences for each
   gene.
   vi. Rank the mean absolute Expression
   Rank differences.
   vii. Repeat steps ii-vi for each cell type.
   viii. Per gene, compute the sum of the
   previous ranks across all cell types, and
   then rank these sums across genes
   such that the highest rank is given to
   the gene with the smallest sum. This is
   the final Rank Invariance value.
   Gene Filtering & TREG Selection
   c
   i,j,k,z is the number of snRNA-seq
   counts for nucleus z for gene i, cell
   type j, and brain region k, and nj,k is
   the number of nuclei for cell type j
   and brain region k
   1. Filter to top 50% expressed genes
   2. Calculate Proportion Zero for each gene across each Brain Region
   & cell type
   3. Filter to genes with a maximum Proportion Zero across groups <
   0.75
   Properties of candidate TREGs in snRNA-seq data
   • Select AKT3, ARID1B, and MALAT1 as candidate TREGs from top RI
   values
   • Observed highly rank invariant expression vs. Housekeeping gene
   POLR2A
   • Candidate TREGs show less variable Expression rank than HK POLR2A
   • Observed strong linear relationship between TREG expression and
   total nuclear RNA (estimated by the log2 sum of all counts) within
   each cell type
   • Three RNAscope probe combinations (TREG or POLR2A + cell type
   markers) used to test the performance of the genes
   • TREG puncta are observed in 86% or more nuclei and in dynamic
   ranges
   • AKT3 expression in higher in transcriptionaly activity gray matter than
   white matter
   • MALAT1 reads are unreliable
   • Rank Invariance is an effective way to find TREGs in
   sn/scRNA-seq data and can be used to identify TREGs
   relevant to a specific tissue or experimental setting
   • AKT3 is an effect TREG in the human brain specifically the
   DLPFC
   • TREGs represent an important class of genes that could be
   used for a variety of assays and downstream analyses
   Puncta vs. Total RNA Expression
   Huuki-Myers et al., BioRxiv, 2022, 10.1101/2022.04.28.489923
   Tran, Maynard et al., Neuron, 2021, 10.1101/2020.10.07.329839
   Indica labs, HALO 3.3 FISH-IF
   TREG paper git repo 10.5281/zendo.6502303
   Gene
   Prop. non-
   zero in
   DLPFC
   snRNA
   Mean
   prop.
   non-zero
   Mean n
   puncta
   AKT3 0.92 0.88 4.09
   ARID1B 0.94 0.86 3.08
   MALAT1 1.00 0.98 2.07
   POLR2A 0.30 0.78 2.75
   Gene β sd Std.
   AKT3 -5.52 5.18 -1.07
   ARID1B -2.63 3.42 -0.77
   MALAT1 -1.22 1.53 -0.8
   POLR2A -3.49 3.34 -1.05
   All genes in
   snRNA-seq -21844.07 15560.76 -1.33
   • AKT3 has most similar slope to
   total expression measured by
   snRNA-seq over observable cell
   types
   • RNA scope + TREG allows the
   comparison of nuclear size and
   total RNA expression
   Experimental Design
   Proportion Zero Equation
   Louise Huuki-Myers Kelsey D. Montgomery Sang Ho Kwon Stephanie C. Page Stephanie C. Hicks Kristen R. Maynard Leonardo Collado-Torres
   

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