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Rahbari, N. N.

Publications and source records attributed to Rahbari, N. N..

3 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↗

Pancreatic cancer patient-derived organoids capture therapy response and tumor evolution

Patient-derived organoids (PDOs) reflect parental tumor features and may represent promising avatars for prognosticating drug response. Here, we recruited 169 patients with pancreatic cancer (PC) and established a living biobank including 83 pharmacotyped PDOs isolated from primary and metastatic, treatment-naive and pretreated PCs. In a core facility setting, the pharmacotyping success rate was 61.5%, with an unmet turnaround time of 32 days. Forty-six patients who underwent a total of 94 therapeutic lines were analyzed, resulting in a pharmacotyping-patient response matching rate of 73.4%. Sensitivity, specificity, positive and negative predictive values were 85.0%, 64.8%, 64.2%, and 85.4%, respectively. Tracing clonal evolution in longitudinal biopsies uncovered therapy-induced genetic alterations and single-nucleus multiomics identified transcriptomic and epigenetic changes associated with abnormal FGF signaling during treatment in one particular tracked study case. Our findings highlight the potential of PDOs as robust tools for drug response prediction and patient modeling to advance functional precision medicine.

cancer biology↗

Comparison of extraction methods for intracellular metabolomics

Measurements of metabolic compounds inside cells or tissues are of high informative potential since they represent the endpoint of biological information flow and a snapshot of the integration of many regulatory processes. However, it requires careful extraction to quantify their abundance. Here we present a comprehensive study using ten extraction protocols on four human sample types (liver tissue, bone marrow, HL60 and HEK cells) targeting 630 metabolites of different chemical classes. We show that the extraction efficiency and stability is highly variable across protocols and tissues by using different quality metrics including the limit of detection and variability between replicates as well as the sum of concentration as a global estimate of extraction stability. The profile of extracted metabolites depends on the used solvents - an observation which has implications for measurements of different sample types and metabolic compounds of interest. To identify the optimal extraction method for future metabolomics studies, the benchmark dataset was implemented in an easy-to-use, interactive and flexible online resource (R/shiny app MetaboExtract).

biochemistry↗