Biology subjects

Lalit, M.

Publications and source records attributed to Lalit, M..

2 recordsLinked to original sources

bioRxiv2022

Nanog organizes transcription bodies

The localization of transcriptional activity in specialized transcription bodies is a hallmark of gene expression in eukaryotic cells. How proteins of the transcriptional machinery come together to form such bodies, however, is unclear. Here, we take advantage of two large, isolated, and long-lived transcription bodies that reproducibly form during early zebrafish embryogenesis, to characterize the dynamics of transcription body formation. Once formed, these transcription bodies are enriched for initiating and elongating RNA polymerase II, as well as the transcription factors Nanog and Sox19b. Analyzing the events leading up to transcription, we find that Nanog and Sox19b cluster prior to transcription, and independently of RNA accumulation. The clustering of transcription factors is sequential; Nanog clusters first, and this is required for the clustering of Sox19b and the initiation of transcription. Mutant analysis revealed that both the DNA-binding domain, as well as one of the two intrinsically disordered regions of Nanog are required to organize the two bodies of transcriptional activity. Taken together, our data suggests that the clustering of transcription factors dictates the formation of transcription bodies. HIGHLIGHTSO_LITranscription factors cluster prior to, and independently of transcription C_LIO_LINanog organizes transcription bodies: it is required for the clustering of Sox19b as well as RNA polymerase II C_LIO_LIThis organizing activity requires its DNA binding domain as well as one of its intrinsically disordered regions C_LIO_LITranscription elongation results in the disassembly of transcription factor clusters C_LI

cell biology↗

bioRxiv2018

ROSeq: A rank based approach to modelling gene expression in single cells

1Systematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single cell expression data. The zero-inflated version of Poisson/Negative Binomial and Log-Normal distributions have emerged as the most popular alternatives due to their ability to accommodate high dropout rates, as commonly observed in single cell data. While the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression-ranks, as robust surrogates for transcript abundance. Here we examined the performance of the Discrete Generalized Beta Distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method, to understand its advantages as compared to some of the existing best practice approaches. Besides striking a reasonable balance between Type 1 and Type 2 errors, we concluded that ROSeq, the proposed differential expression test is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq, and made it available on the Bioconductor platform.

genomics↗