Biology subjects

Kumar, M. P.

Publications and source records attributed to Kumar, M. P..

4 recordsLinked to original sources

bioRxiv2020

Clustered Loss of Dendritic Spines Characterizes Encoding of Related Memory

Generation of new spines is often thought of as a correlate of memory and loss of spines is considered representing memory loss. Contrary to common belief, we observe that spine loss has functional value in distinctly encoding related life events rather than causing memory loss. Using spatial autocorrelation of dendritic morphology obtained from in vivo longitudinal imaging, we show that clustered loss, rather than gain, of new spines characterizes the formation of related memory. This spatially selective dendritic spine loss occurs closer to new spines formed during the acquisition of initial memory. Thus, enabling the dendrites to store multiple memories and their inter relationship. Remarkably, we find acquisition of related memory in the absence of NMDAR activation increases the fraction of such correlated spine loss.

neuroscience↗

bioRxiv2020

Immediate early gene expression dynamics in vivo segregates neuronal ensemble of multiple memories.

Immediate early genes (IEGs) are widely used as a marker for neuronal plasticity. Here, we model the dynamics of IEG expression as a consecutive, irreversible first order reaction with a limiting substrate. We show that such a model, together with two-photon in vivo imaging of IEG expression, can be used to identify distinct neuronal subsets representing multiple memories. We image retrosplenial cortex (RSc) of cFOS-GFP transgenic mice to follow the dynamics of cellular changes resulting from both seizure and contextual fear conditioning behaviour. The analytical expression allowed us to segregate the neurons based on their temporal response to one specific behavioural event, thereby improving the sensitivity of detecting plasticity related neurons. This enables us to establish representation of context in RSc at the cellular scale following memory acquisition. Thus, we obtain a general method which distinguishes neurons that took part in multiple temporally separated events, by measuring fluorescence from individual neurons in live mice. SummaryIdentifying neuronal ensemble associated with different memories is vital in modern neuroscience. Meenakshi et al model and use the temporal expression dynamics of IEGs rather than thresholded intensities of the probes to identify the neurons encoding different memory in vivo. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/423270v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1d6c197org.highwire.dtl.DTLVardef@3fca7aorg.highwire.dtl.DTLVardef@64615org.highwire.dtl.DTLVardef@1e30dbc_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2019

An Inter-Species Translation Model Implicates Integrin Signaling in Infliximab-Resistant Colonic Crohn’s Disease

Anti-TNF therapy resistance is a major clinical challenge in Crohns Disease (CD), partly due to insufficient understanding of disease-site, protein-level mechanisms of CD and anti-TNF treatment resistance. Although some proteomics data from CD mouse models exists, data type and phenotype discrepancies contribute to confounding attempts to translate between preclinical animal models of disease and human clinical cohorts. To meet this important challenge, we develop and demonstrate here an approach called Translatable Components Regression (TransComp-R) to overcome inter-species and trans-omic discrepancies between CD mouse models and human subjects. TransComp-R combines CD mouse model proteomic data with patient pre-treatment transcriptomic data to identify molecular features discernable in the mouse data predictive of patient response to anti-TNF therapy. Interrogating the TransComp-R models predominantly revealed upregulated integrin pathway signaling via collagen-binding integrin ITGA1 in anti-TNF resistant colonic CD (cCD) patients. Toward validation, we performed single-cell RNA sequencing on biopsies from a cCD patient and analyzed publicly available immune cell proteomics data to characterize the immune and intestinal cell types contributing to anti-TNF resistance. We found that ITGA1 is indeed expressed in colonic T-cell populations and that interactions between collagen-binding integrins on T-cells and colonic cell types expressing secreted collagens are associated with anti-TNF therapy resistance. Biologically, TransComp-R linked previously disparate observations about collagen and ITGA1 signaling to a potential therapeutic avenue for overcoming anti-TNF therapy resistance in cCD. Methodologically, TransComp-R provides a flexible, generalizable framework for addressing inter-species, inter-omic, and inter-phenotypic discrepancies between animal models and patients to deliver translationally relevant biological insights.\n\nOne Sentence SummaryBrubaker et al. implicate dysregulated collagen-binding integrin signaling in resistance to anti-TNF therapy in Crohns Disease by developing a mouse-proteomic to human-transcriptomic translation model and confirm the associated inter-cellular signaling network using single-cell RNA sequencing.

systems biology↗

bioRxiv2019

Robustness and applicability of functional genomics tools on scRNA-seq data

Many tools have been developed to extract functional and mechanistic insight from bulk transcriptome profiling data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events, low library sizes and a comparatively large number of samples/cells. It is thus not clear if functional genomics tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. To address this question, we performed benchmark studies on in silico and in vitro single-cell RNA-seq data. We included the bulk-RNA tools PROGENy, GO enrichment and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compared them against the tools AUCell and metaVIPER, designed for scRNA-seq. For the in silico study we simulated single cells from TF/pathway perturbation bulk RNA-seq experiments. Our simulation strategy guarantees that the information of the original perturbation is preserved while resembling the characteristics of scRNA-seq data. We complemented the in silico data with in vitro scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on both the simulated and real data revealed comparable performance to the original bulk data. Additionally, we showed that the TF and pathway activities preserve cell-type specific variability by analysing a mixture sample sequenced with 13 scRNA-seq different protocols. Our analyses suggest that bulk functional genomics tools can be applied to scRNA-seq data, outperforming dedicated single cell tools. Furthermore we provide a benchmark for further methods development by the community.

bioinformatics↗