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Biology subjects

Neavin, D. R.

Publications and source records attributed to Neavin, D. R..

2 recordsLinked to original sources

HLiCA: An integrated cell atlas of the healthy human liver

The human liver is composed of a heterogeneous mix of cell types. How these distinct populations contribute individually and collectively to liver function remains poorly understood. Although single-cell technologies have advanced our understanding of liver biology, individual studies have often been limited by small donor cohorts and inconsistent cell type annotations. Integrating multiple datasets can overcome these challenges and better capture biological variability. We present the Human Liver Cell Atlas (HLiCA), an integrated reference of non-disease liver cells assembled from eight datasets across six research centers, encompassing more than 525,000 cells from 110 donors. Developed in collaboration with the Human Cell Atlas Liver Bionetwork, the HLiCA incorporates expert-curated cell annotations refined through community feedback and dedicated cell type annotation meetings. The HLiCA classifies cells into six lineages and expands the cell type resolution to include 47 distinct cell types. Starting from raw sequencing reads, we realigned all data and performed rigorous benchmarking to ensure robust integration across technical and biological variables. Genetic ancestry was inferred for all samples to evaluate the range of ancestral backgrounds represented in the atlas. The expanded cell type annotation enabled identification of previously unrecognized liver cell types, including NRXN1+ stromal cells. Their presence was validated using spatial transcriptomics, which localized NRXN1+ stromal cells to periportal regions. With the number of donors included in the HLiCA we were able to examine cell type specific associations with demographic covariates. In hepatocytes, drug metabolism genes showed differential expression between sexes, and in cholangiocytes, mucus-production genes varied with age. As the largest and most genetically diverse human liver cell atlas to date, the HLiCA provides a comprehensive, well-annotated reference for the field, annotated by expert consensus. This resource will enable deeper interrogation of liver cellular diversity, architecture, and function in the healthy human liver and serve as a reference to understand changes that occur with disease.

genomics↗

Classification of indeterminate and off-target cell types within human kidney organoid differentiation

Human pluripotent stem cell-derived organoids are multicellular models of developing tissues proposed to recapitulate developmental stages of lineage commitment across time. Assessing how accurately protocols recapitulate development is challenged by the lack of accurate reference data sets for early human development, the presence of off-target states and the potential to form cellular states not present in vivo. This study addresses these challenges with respect to differentiation of human pluripotent stem cells to kidney organoids. Based on a factorial single-cell transcriptomic analysis across a 27-day differentiation protocol, including 150,957 cells collected at five time points, we present a comprehensive classification of predicted identity, representing an in vitro temporal transcriptional atlas. For early stages, cellular identity was defined in relation to existing human developmental data across time and organ system. As renal structures arose, this was coupled with the kidney-specific classification methods. In this way we identify predicted cellular states, predictable off-target cell types based on embryology and transitional mesodermal populations with no clear in vivo equivalent. An analysis of all existing single cell data on this same protocol suggests the presence of the same off-target trajectories irrespective of cell line or laboratory. This study provides a valuable resource to better identify and understand how off-target populations arise in vitro, benefiting the advancement of kidney organoid technologies towards therapeutic outcomes.

cell biology↗