Publications by authors named "Bobby Ranjan"

The gut microbiota operates at the interface of host-environment interactions to influence human homoeostasis and metabolic networks. Environmental factors that unbalance gut microbial ecosystems can therefore shape physiological and disease-associated responses across somatic tissues. However, the systemic impact of the gut microbiome on the germline-and consequently on the F offspring it gives rise to-is unexplored.

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  • Type 1 diabetes (T1DM) is an autoimmune disease that destroys insulin-producing cells in the pancreas, and this study aimed to evaluate immune cell changes at the single-cell level for the first time.
  • Researchers analyzed immune cells from 46 T1DM patients and 31 controls, finding significant gene expression alterations in immune cells that were greater than those observed in another autoimmune disease, systemic lupus erythematosus (SLE).
  • The study developed a new metric, the T1DM metagene z-score (TMZ score), which could categorize patients, correlate with existing immune markers, and help inform treatment responses, highlighting the major immune shifts present in T1DM.
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Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even resulting in poorer clustering accuracy than without feature selection. Moreover, existing methods ignore information contained in gene-gene correlations.

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The transcriptomic diversity of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Unsupervised clustering of SC transcriptomes, which is the default technique for defining cell types, is prone to group cells by technical, rather than biological, variation. Compared to de-novo (unsupervised) clustering, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects and data quality artifacts.

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  • Clustering is vital in analyzing single-cell data, with unsupervised methods relying on gene expression and supervised methods using a reference of labeled transcriptomes, both offering unique advantages and limitations.
  • SCCONSENSUS is a new framework that combines both clustering approaches to create a consensus clustering, enhancing results by integrating unsupervised and supervised findings and refining clusters through differential gene expression.
  • The framework improves cluster separation and consistency, resulting in more accurate cell type identification, and is available for free on GitHub.
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Immunotherapy for metastatic colorectal cancer is effective only for mismatch repair-deficient tumors with high microsatellite instability that demonstrate immune infiltration, suggesting that tumor cells can determine their immune microenvironment. To understand this cross-talk, we analyzed the transcriptome of 91,103 unsorted single cells from 23 Korean and 6 Belgian patients. Cancer cells displayed transcriptional features reminiscent of normal differentiation programs, and genetic alterations that apparently fostered immunosuppressive microenvironments directed by regulatory T cells, myofibroblasts and myeloid cells.

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Background: Alzheimer's disease (AD) is a progressive neurological disorder, recognized as the most common cause of dementia affecting people aged 65 and above. AD is characterized by an increase in amyloid metabolism, and by the misfolding and deposition of β-amyloid oligomers in and around neurons in the brain. These processes remodel the calcium signaling mechanism in neurons, leading to cell death via apoptosis.

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