Cardiello, J. F. | Search bioRxiv

bioRxiv2023

Characterizing Primary transcriptional responses to short term heat shock in paired fraternal lymphoblastoid lines with and without Down syndrome.

Heat shock stress induces genome wide changes in transcription regulation, activating a coordinated cellular response to enable survival. Using publicly available transcriptomic and proteomic data sets comparing individuals with and without trisomy 21, we noticed many heat shock genes are up-regulated in blood samples from individuals with trisomy 21. Yet no major heat shock response regulating transcription factor is encoded on chromosome 21, leaving it unclear why trisomy 21 itself would cause a heat shock response, or how it would impact the ability of blood cells to subsequently respond when faced with heat shock stress. To explore these issues in a context independent of any trisomy 21 associated co-morbidities or developmental differences, we characterized the response to heat shock of two lymphoblastoid cell lines derived from brothers with and without trisomy 21. To carefully compare the chromatin state and the transcription status of these cell lines, we measured nascent transcription, chromatin accessibility, and single cell transcript levels in the lymphoblastoid cell lines before and after acute heat shock treatment. The trisomy 21 cells displayed a more robust heat shock response after just one hour at 42{degrees}C than the matched disomic cells. We suggest multiple potential mechanisms for this increased heat shock response in lymphoblastoid cells with trisomy 21 including the possibility that cells with trisomy 21 may exist in a hyper-reactive state due to chronic stresses. Whatever the mechanism, abnormal heat shock response in individuals with Down syndrome may hobble immune responses during fever and contribute to health problems in these individuals.

bioinformatics↗

bioRxiv2022

Accurate genotype-based demultiplexing of single cell RNA sequencing samples from non-human animals

Single cell sequencing technologies (scRNA-seq, scATAC-seq, etc.) have revolutionized the study of complex tissues and unique organisms, providing researchers with a much needed species agnostic tool to study biological processes at the cellular level. To date, scRNA-seq technologies are expensive, require sufficient cell quantities, and need biological replicates to avoid batch effects or artifactual results. Pooling cells from multiple individuals into a single scRNA-seq library can address these problems. However, sample labeling protocols for facilitating the computational separation of pooled scRNA-seq samples, termed demultiplexing, have undesirable limitations, particularly in resource-limited organisms. One promising solution developed for use in humans exploits the genetic diversity between individuals (i.e., single nucleotide polymorphisms (SNP)) to demultiplex pooled scRNA-seq samples. The use of SNP-based demultiplexing methods has not been validated for use in non-human species, but the widespread use of SNP-based demuxers would greatly facilitate research in commonly used, emerging, and more obscure species. In this study we applied SNP-based demultiplexing algorithms to pooled scRNA-seq datasets from numerous species and applied diverse ground truth confirmation assays to validate genetic demultiplexing results. SNP-based demultiplexers were found to accurately demultiplex pooled scRNA-seq data from species including zebrafish, African green monkey, Xenopus laevis, axolotl, Pleurodeles waltl, and Notophthalmus viridescens. Our results demonstrate that SNP-based demultiplexing of unlabeled, pooled scRNA-seq samples can be used with confidence in all of the species studied in this work. Further, we show that the only genomic resource required for this approach is the single-cell sequencing data and a de novo transcriptome. The incorporation of pooling and SNP-demultiplexing into scRNA-seq study designs will greatly increase the reproducibility and experimental options for studying species previously limited by technical uncertainties, computational hurdles, or limited cell quantities.

bioinformatics↗