Biology subjects

Siegel, D. A.

Publications and source records attributed to Siegel, D. A..

3 recordsLinked to original sources

bioRxiv2020

MPRAudit Quantifies the Fraction of Variance Described by Unknown Features in Massively Parallel Reporter Assays

Transformative advances in molecular technologies, such as massively parallel reporter assays (MPRAs) and CRISPR screens, can efficiently characterize the effects of genetic and genomic variation on cellular phenotypes. Analysis approaches to date have focused on identifying individual genomic regions or genetic variants that perturb a phenotype of interest. In this work, we develop a wholistic framework (MPRAudit) to determine the global contribution of sequence to phenotypic variation across subsets of the entire experiment, opening the door to myriad novel analyses. For example, MPRAudit can reliably estimate the upper limit of predictive performance, the fraction of variation attributed to specific biological categories, and the total contribution of experimental noise. We demonstrate through simulation and application to several types of real MPRA data sets how MPRAudit can lead to an improved understanding of experimental quality, molecular biology, and guide future research. Applying MPRAudit to real MPRA data, we observe that sequence variation is the primary driver of outcome variability, but that known biological categories explain only a fraction of this variance. We conclude that our understanding of how sequence variation impacts phenotype, even at the level of MPRAs, remains open to further scientific discovery.

bioinformatics↗

bioRxiv2020

No detectable alloreactive transcriptional responses during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells

Single-cell RNA sequencing (scRNA-seq) provides high-dimensional measurement of transcript counts in individual cells. However, high assay costs limit the study of large numbers of samples. Sample multiplexing technologies such as antibody hashing and MULTI-seq use sample-specific sequence tags to enable individual samples (e.g., different patients) to be sequenced in a pooled format before downstream computational demultiplexing. Critically, no study to date has evaluated whether the mixing of samples from different donors in this manner results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self immune antigens). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures. Here, we compared the expression profiles of peripheral blood mononuclear cells (PBMCs) from a single donor with and without pooling with PBMCs isolated from other donors with different blood types. We find that there was no evidence of alloreactivity in the multiplexed samples following three distinct multiplexing workflows (antibody hashing, MULTI-seq, and in silico genotyping using souporcell). Moreover, we identified biases amongst antibody hashing sample classification results in this particular experimental system, as well as gene expression signatures linked to PBMC preparation method (e.g., Ficoll-Paque density gradient centrifugation with or without apheresis using Trima filtration).

genomics↗

bioRxiv2020

Massively Parallel Analysis of Human 3' UTRs Reveals that AU-Rich Element Length and Registration Predict mRNA Destabilization

AU-rich elements (AREs) are 3' UTR cis-regulatory elements that regulate the stability of mRNAs. Consensus ARE motifs have been determined, but little is known about how differences in 3' UTR sequences that conform to these motifs affect their function. Here we use functional annotation of sequences from 3' UTRs (fast-UTR), a massively parallel reporter assay (MPRA), to investigate the effects of 41,288 3' UTR sequence fragments from 4,653 transcripts on gene expression and mRNA stability. The library included 9,142 AREs, and incorporated a set of fragments bearing mutations in each ARE. Our analyses demonstrate that the length of an ARE and its registration (the first and last nucleotides of the repeating ARE motif) have significant effects on gene expression and stability. Based on this finding, we propose improved ARE classification and concomitant methods to categorize and predict the effect of AREs on gene expression and stability. Our new approach explains 64{+/-}13% of the contribution of AREs to the stability of human 3' UTRs in Jurkat cells and predicts ARE activity in an unrelated cell type. Finally, to investigate the advantages of our general experimental design for annotating 3' UTR elements we examine other motifs including constitutive decay elements (CDEs), where we show that the length of the CDE stem-loop has a significant impact on steady-state expression and mRNA stability. We conclude that fast-UTR, in conjunction with our analytical approach, can produce improved yet simple sequence-based rules for predicting the activity of human 3' UTRs containing functional motifs.

genomics↗