Biology subjects

Hingorani, R.

Publications and source records attributed to Hingorani, R..

3 recordsLinked to original sources

bioRxiv2026

Deep phenomics of African green monkey and Baboon PBMCs using a 30-marker spectral flow cytometry panel

Vaccines are the best way to prevent and combat diseases. Non-human primate (NHP) models have proven to be the gold standard model for studying the efficacy of certain vaccine candidates prior to human trials. Cell-mediated immunity serves as a surrogate marker for predicting vaccine efficacy. Despite the availability of deep-immunophenotyping panels for T cells in the peripheral blood of humans, their application to NHPs, particularly African Green Monkeys (AGMs) and baboons, remains limited. Notably, AGMs are valuable for HIV and COVID-19 research, while baboons are utilized for pertussis research as a natural infection model and vaccine trial. To overcome such limitation, using spectral flow cytometry, we developed a 30-parameter panel to profile major immune cell subsets (B cells, NK cells, DCs, monocytes) either in AGM or baboon PBMCs. We also profiled T cells, memory phenotypes, functional cytokines by tracking established Th1 markers IFN{gamma}, TNF and IL-2; Th2 markers IL-4/5/13 and the Th17 marker, IL-17, activation-induced markers (AIMs) and FoxP3+ Tregs in both AGM and baboon PBMCs in steady state as well as in mitogen stimulated conditions. For AIMs, CD69, CD134 (OX40) and CD137 (4-1BB) are included and validated in both NHP species. This panel holds potential for exploratory analysis of the impact of these functional cytokines, AIMs, or Tregs on host immunity following infection or vaccination. The panels high level of customization empowers researchers to tailor it to their specific research needs.

immunology↗

bioRxiv2026

Deep phenomics of human PBMCs using a 31-marker spectral flow cytometry panel

Functional cytokines orchestrate leukocyte-mediated innate and adaptive immune responses within the host following infection or immunization. In contrast, lymphocyte-mediated activation-induced markers (AIMs) are predominantly associated with adaptive immune responses. Regulatory T cells (Tregs) primarily function to balance the immune response by preventing excessive inflammation and maintaining self-tolerance. Currently, conventional intracellular cytokine tracking or cytokine-independent AIMs methodology is available for studying antigen-specific lymphocyte or T cell responses in humans. However, this approach faces challenges due to the difficulty in simultaneously deep immunophenotyping the key functional cytokine signatures, AIMs, and nuclear signature of FoxP3+ Tregs. Using spectral flow cytometry, we developed a 31-parameter panel to profile simultaneously major immune cell subsets (B cells, NK cells, DCs, monocytes) along with T cells, memory phenotypes, functional cytokines by tracking established Th1 markers IFN{gamma}, TNF and IL-2; Th2 markers IL-4/5/13 and the Th17 marker, IL-17, AIMs and FoxP3+ Tregs in human PBMCs in steady state as well as in mitogen stimulated conditions. For AIMs, CD69, CD134 (OX40) and CD137 (4-1BB) are included and validated. We also re-validated our panel to profile antigen specific Th1/2/17 cells or AIMs distribution after peptide stimulation. This optimized multicolor spectral flow panel allows a detailed immune profiling of functional cytokines, AIMs and FoxP3+ Tregs in the T cell compartment from human and might be useful for exploratory analysis of how these functional cytokines or AIMs or Tregs shape host immunity after infection or vaccination. Our panel is highly customizable, allowing researchers to tailor it to their specific requirements.

immunology↗

bioRxiv2026

Estimation of neuronal tuning for word meaning from passively recorded naturalistic speech

The ability to derive neural-level language coding models holds great scientific and clinical potential. Current approaches are limited by the scale and ethological validity of input data; applications requiring large, rare, or naturalistic samples in particular would benefit from the ability to infer neural coding from incidental everyday speech. Here we present a novel pipeline designed to leverage spontaneous and incidental naturalistic speech. This pipeline performs transcription, segmentation, and video-assisted diarization, as well as alignment and spike detection of neural data. We apply this pipeline to a dataset derived from 21 patients (6+ days each, over 800 hours and 5 million words total). We benchmark both encoding and decoding models against extensive and rare ground-truth control datasets consisting of human-curated word-level temporal alignment and manually sorted spikes. We further validate our approach by quantifying representational drift, effect of dataset size, and differences between six brain areas. Together, these findings demonstrate that incidental natural speech is sufficiently processed in the brain to enable the estimation neural-level embeddings.

neuroscience↗