Biology subjects

Murti, A.

Publications and source records attributed to Murti, A..

4 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2024

Perinatal liver inflammation is associated with persistent elevation of CXCL10 and its canonical receptor CXCR3 on common myeloid progenitors

Biliary atresia (BA) is a leading cause of liver failure in infants. Despite effective surgical drainage, patients with BA exhibit attenuated immune responses to childhood vaccines, suggesting there are long-lasting alterations to immune function. The perinatal liver is home to hematopoietic stem and progenitor cells (HSPCs) and serves as the epicenter for rapidly progressive and significantly morbid inflammatory diseases like BA. We have previously established the role of neonatal myeloid progenitors in the pathogenesis of perinatal liver inflammation (PLI) and hypothesize that PLI leads to long-term changes to HSPCs in mice that recovered from PLI. To test this hypothesis, we compared the changes that occur to HSPCs and mature myeloid populations in the bone marrow of adult mice during homeostasis and during PLI. Our results demonstrate that HSPCs from animals that recover from PLI ("PLI-recovered") undergo long-term expansion with a reduced proliferative capacity. Notably, PLI leads to persistent activation of common myeloid progenitors through the involvement of CXCL10 and its canonical receptor, CXCR3. Our data suggests that the CXCR3-CXCL10 axis may mediate the changes in HSPCs that lead to altered immune function observed in BA, providing support for a targetable pathway to mitigate the detrimental long-term immune effects observed in patients with BA.

immunology↗

bioRxiv2023

Modeling and Analysis of a Cell-Free Gluconate Responsive Biosensor

Cell-free synthetic systems are composed of the parts required for transcription and translation processes in a buffered solution. Thus, unlike living cells, cell-free systems are amenable to rapid adjustment of the reaction composition and easy sampling. Further, because cellular growth and maintenance requirements are absent, all resources can go toward synthesizing the product of interest. Recent improvement in key performance metrics, such as yield, reaction duration, and portability, has increased the space of possible applications open to cell-free systems and lowered the time required to design-build-test new circuitry. One promising application area is biosensing. This study describes developing and modeling a D-gluconate biosensor circuit operating in a reconstituted cell-free system. Model parameters were estimated using time-resolved measurements of the mRNA and protein concentration with and without the addition of D-gluconate. Sensor performance was predicted using the model for D-gluconate concentrations not used in model training. The model predicted the transcription and translation kinetics and the dose response of the circuit over several orders of magnitude of D-gluconate concentration. Global sensitivity analysis of the model parameters gave detailed insight into the operation of the sensor circuit. Taken together, this study reported an in-depth, systems-level analysis of a D-gluconate biosensor circuit operating in a reconstituted cell-free system. This circuit could be used directly to estimate D-gluconate or as a subsystem in a more extensive synthetic gene expression program.

synthetic biology↗

bioRxiv2022

Comparative analysis of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

Spatial transcriptomics extends single cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) can achieve single-cell resolution, directly mapping single cell identities to spatial positions. MERFISH produces an intrinsically different data type than scRNA-seq and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas as well as from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that compared to scRNA-seq, MERFISH provides a quantitatively comparable method for measuring single-cell gene expression and can robustly identify cell types without the need for computational integration with scRNA-seq reference atlases.

bioinformatics↗