Biology subjects

Welch, L. R.

Publications and source records attributed to Welch, L. R..

3 recordsLinked to original sources

bioRxiv2022

3D genome topologies distinguish pluripotent epiblast and primitive endoderm cells in the mouse blastocyst

The development of embryonic cell lineages is tightly controlled by transcription factors that regulate gene expression and chromatin organisation. To investigate the specialisation of 3D genome structure in pluripotent or extra-embryonic endoderm lineages, we applied Genome Architecture Mapping (GAM) in embryonic stem (ES) cells, extra-embryonic endoderm (XEN) stem cells, and in their in vivo counterparts, the epiblast (Epi) and primitive endoderm (PrE) cells, respectively. We discover extensive differences in 3D genome topology including the formation domain boundaries that differ between Epi and PrE lineages, both in vivo and in vitro, at lineage commitment genes. In ES cells, Sox2 contacts other active regions enriched for NANOG and SOX2 binding sites. PrE-specific genes, such as Lama1 and Gata6, form repressive chromatin hubs in ES cells. Lama1 activation in XEN or PrE cells coincides with its extensive decondensation. Putative binding sites for OCT4 and SNAIL, or GATA4/6, distinguish chromatin contacts unique to embryonic or extra-embryonic lineages, respectively. Overall, 3D genome folding is highly specialised in early development, especially at genes encoding factors driving lineage identity. HighlightsO_LIES and XEN cells have specialised 3D genome structures C_LIO_LIGAM applied in the blastocyst distinguishes Epi and PrE genome structures C_LIO_LILineage specific genes establish cell-type specific chromatin contacts C_LIO_LISpecific chromatin contacts feature putative bindings sites for GATA4/6 in XEN cells and SNAIL in ES cells C_LI

developmental biology↗

bioRxiv2020

Single-Cell Analysis of the 3D Topologies of Genomic Loci Using Genome Architecture Mapping

Although each cell within an organism contains a nearly identical genome sequence, the three-dimensional (3D) packing of the genome varies among individual cells, influencing cell-type-specific gene expression. Genome Architecture Mapping (GAM) is the first genome-wide experimental method for capturing 3D proximities between any number of genomic loci without ligation. GAM overcomes several limitations of 3C-based methods by sequencing DNA from a large collection of thin sections sliced from individual nuclei. The GAM technique measures locus co-segregation, extracts radial positions, infers chromatin compaction, requires small numbers of cells, does not depend on ligation, and provides rich single-cell information. However, previous analyses of GAM data focused exclusively on population averages, neglecting the variation in 3D topology among individual cells. We present the first single-cell analysis of GAM data, demonstrating that the slices from individual cells reveal intercellular heterogeneity in chromosome conformation. By simultaneously clustering both slices and genomic loci, we identify topological variation among single cells, including differential compaction of cell cycle genes. We also develop a geometric model of the nucleus, allowing prediction of the 3D positions of each slice. Using GAM data from mouse embryonic stem cells, we make new discoveries about the structure of the major mammalian histone gene locus, which is incorporated into the Histone Locus Body (HLB), including structural fluctuations and putative causal molecular mechanisms. Our methods are packaged as SluiceBox, a toolkit for mining GAM data. Our approach represents a new method of investigating variation in 3D genome topology among individual cells across space and time.

bioinformatics↗

bioRxiv2017

Bioinformatics Core Competencies for Undergraduate Life Sciences Education

Bioinformatics is becoming increasingly central to research in the life sciences. However, despite its importance, bioinformatics skills and knowledge are not well integrated in undergraduate biology education. This curricular gap prevents biology students from harnessing the full potential of their education, limiting their career opportunities and slowing genomic research innovation. To advance the integration of bioinformatics into life sciences education, a framework of core bioinformatics competencies is needed. To that end, we here report the results of a survey of life sciences faculty in the United States about teaching bioinformatics to undergraduate life scientists. Responses were received from 1,260 faculty representing institutions in all fifty states with a combined capacity to educate hundreds of thousands of students every year. Results indicate strong, widespread agreement that bioinformatics knowledge and skills are critical for undergraduate life scientists, as well as considerable agreement about which skills are necessary. Perceptions of the importance of some skills varied with the respondents degree of training, time since degree earned, and/or the Carnegie classification of the respondents institution. To assess which skills are currently being taught, we analyzed syllabi of courses with bioinformatics content submitted by survey respondents. Finally, we used the survey results, the analysis of syllabi, and our collective research and teaching expertise to develop a set of bioinformatics core competencies for undergraduate life sciences students. These core competencies are intended to serve as a guide for institutions as they work to integrate bioinformatics into their life sciences curricula.\n\nSignificance StatementBioinformatics, an interdisciplinary field that uses techniques from computer science and mathematics to store, manage, and analyze biological data, is becoming increasingly central to modern biology research. Given the widespread use of bioinformatics and its impacts on societal problem-solving (e.g., in healthcare, agriculture, and natural resources management), there is a growing need for the integration of bioinformatics competencies into undergraduate life sciences education. Here, we present a set of bioinformatics core competencies for undergraduate life scientists developed using the results of a large national survey and the expertise of our working group of bioinformaticians and educators. We also present results from the survey on the importance of bioinformatics skills and the current state of integration of bioinformatics into biology education.

bioinformatics↗