Biology subjects

Deshmukh, S. S.

Publications and source records attributed to Deshmukh, S. S..

7 recordsLinked to original sources

bioRxiv2025

Patient-Derived Three-Dimensional Lung Tumor Models to Evaluate Response to Immunotherapy

Novel preclinical models that better mimic the in vivo tumor microenvironment are needed for advanced understanding of tumor biology and resistance/response to therapy. Herein, we report development of a novel ex vivo patient-derived three-dimensional lung tumor model (3D-LTM), that maintains features of human extracellular matrix, cell-cell interactions, and tissue architecture to evaluate a rapid response to immune checkpoint inhibitors (ICI). Within this model system, we recapitulated the heterogeneity of response to immunotherapy observed in non-small cell lung cancer (NSCLC) patients and defined signatures associated with response for predicting early response of ICI in patients. Spatial transcriptomics of the 3D-LTMs identified positive correlation of CD8+ T cell populations, CD4+ memory T cells, mast cells, NK cells, endothelial cells and non-classical monocytes with response status, whereas macrophages negatively correlated with response status. Pathway analysis of gene expression showed that chemokine signaling related pathways were activated in responder 3D-LTM tissues, whereas suppression of antigen presentation-related pathways and activation of Treg differentiation-related pathways was associated with 3D-LTMs that were not considered responders. This model system has utility for rapid testing of novel immune directed therapy outcomes and for developing biomarkers of ICI response in NSCLC.

cancer biology↗

bioRxiv2024

A unified model of hippocampal spatial and object cells involving bidirectionally coupled Lateral and Medial Entorhinal Cortical layers

Popularly referred to as the GPS of the brain, the hippocampus has a variety of neurons that encode spatial properties of the environment. These spatial cells of the hippocampus may be broadly placed under two categories - those that encode spatial locations (e.g. place cells, grid cells etc) and those that encode spatial objects (eg. Object-sensitive cells. Object-trace cells etc). There are computational models that explain emergence of specific types of spatial cells, but it is challenging to construct integrative models that can demonstrate the emergence of the complete range of spatial cells both space and object type. We present a simple, unified computational model that explains the emergence of a wide variety of object- and spatially-sensitive neurons in the hippocampus. The model is essentially a deep neural network that combines visual and path integration information. The visual information is received by a part of the model that is analogous to Lateral Entorhinal Cortex (LEC) and path integration information is received by a layer analogous to Medial Entorhinal Cortex (MEC). In order to arrive at a consistent estimate of position, LEC and MEC in the model are connected laterally using a Graph Neural Network. The model is trained to predict position, orientation and reward of a simulated agent. The agent explores a box-like environment with colored walls and objects on the floor and is rewarded based on its encounters with objects. The model demonstrates the emergence of the following 7 types of spatial and object cells - place, grid, border, object, object-sensitive, object-vector and, object-trace cells. The model findings compare favorably with a large body of experimental literature on hippocampal spatial cells.

neuroscience↗

bioRxiv2022

Comparable theta phase coding dynamics along the transverse axis of CA1

Topographical projection patterns from the entorhinal cortex to area CA1 of the hippocampus have led to a hypothesis that proximal CA1 (pCA1, closer to CA2) is spatially more selective than distal CA1 (dCA1, closer to the subiculum). While earlier studies have shown evidence supporting this hypothesis, we recently showed that this difference does not hold true under all experimental conditions. In a complex environment with distinct local texture cues on a circular track and global visual cues, pCA1 and dCA1 display comparable spatial selectivity. Correlated with the spatial selectivity differences, the earlier studies also showed differences in theta phase coding dynamics between pCA1 and dCA1 neurons. Here we show that there are no differences in theta phase coding dynamics between neurons in these two regions under the experimental conditions where pCA1 and dCA1 neurons are equally spatially selective. We also show that dCA1 local field potentials (LFPs) show higher power in theta range compared to pCA1 LFPs. These findings challenge the established notion of dCA1 being inherently less spatially selective and theta modulated than pCA1 and suggest that theta-mediated activation of the CA1 sub-networks to represent space is task-dependent.

neuroscience↗

bioRxiv2020

Distal CA1 maintains a more coherent spatial representation than proximal CA1 when local and global cues conflict

Entorhinal cortical projections show segregation along the transverse axis of CA1, with the medial entorhinal cortex (MEC) sending denser projections to proximal CA1 (pCA1) and the lateral entorhinal cortex (LEC) sending denser projections to distal CA1 (dCA1). Previous studies have reported functional segregation along the transverse axis of CA1 correlated with the functional differences in MEC and LEC. pCA1 shows higher spatial selectivity than dCA1 in these studies. We employ a double rotation paradigm, which creates an explicit conflict between local and global cues, to understand differential contributions of these reference frames to the spatial code in pCA1 and dCA1. We show that pCA1 and dCA1 respond differently to this local-global cue conflict. pCA1 shows incoherent response consistent with the strong conflicting inputs it receives from MEC and distal CA3 (dCA3). In contrast, dCA1 shows a more coherent rotation with global cues. In addition, pCA1 and dCA1 display comparable levels of spatial selectivity in this study. This finding differs from the previous studies, perhaps due to richer sensory information available in our behavior arena. Together these observations indicate that the functional segregation along proximodistal axis of CA1 is not merely of the amount of spatial selectivity but that of the nature of the different inputs utilized to create and anchor spatial representations.

neuroscience↗

bioRxiv2019

Differential propagation of ripples along the proximo-distal and septo-temporalaxes of dorsal CA1 of rats

The functional connectivity of the hippocampus with its primary cortical input, the entorhinal cortex, is organized topographically. In area CA1 of the hippocampus, this leads to different functional gradients along the proximo-distal and septo-temporal axes of spatial/sensory responsivity and spatial resolution respectively. CA1 ripples, a network phenomenon, allows us to test whether the hippocampal neural network shows corresponding gradients in functional connectivity along the two axes. We studied the occurrence and propagation of ripples across the entire proximo-distal axis along with a comparable spatial range of the septo-temporal axis of dorsal CA1. We observed that ripples could occur at any location, but their probability of co-occurrence and amplitude decreased with increasing distance from the reference tetrode. This reduction was greater along the proximo-distal axis than the septo-temporal axis. Furthermore, we found that ripples propagate primarily along the proximo-distal axis. Thus, over a spatial scale of ~1.5 mm, the network is anisotropic along the two axes, complementing the topographically organized cortico-hippocampal connections.

neuroscience↗

bioRxiv2018

The influence of truncating the carboxy-terminal amino acid residues of Streptococcal enolase on its ability to interact with canine plasminogen.

The native octameric structure of streptococcal enolase from Streptococcus pyogenes increasingly dissociates as amino acid residues are removed one by one from the carboxy-terminus. These truncations gradually convert native octameric enolase into monomers and oligomers. In this work, we investigated how these truncations influence the interaction between Streptococcal enolase and canine plasminogen. We used dual polarization interferometry (DPI), localized surface plasmon resonance (LSPR), and sedimentation velocity analytical ultracentrifugation (AUC) to study the interaction. The DPI was our first technique, was performed on all the truncations and used one exclusive kind of chip. The LSRP was used to show that the DPI results were not dependent on the type of chip used. The AUC was required to show that our surface results were not the result of selecting a minority population in any given sample; the majority of the protein was responsible for the binding phenomenon we observed. By comparing results from these techniques we identified one detail that is essential for streptococcal enolase to bind plasminogen: In our hands the individual monomers bind plasminogen; dimers, trimers, tetramers may or may not bind, the fully intact, native, octamer does not bind plasminogen. We also evaluated the contribution to the equilibrium constant made by surface binding as well as in solution. On a surface, the association coefficient is about twice that in solution. The difference is probably not significant. Finally, the fully octameric form of the protein that does not contain a hexahis N-terminal peptide does not bind to a silicon oxynitride surface, does not bind to a Au-nanoparticle surface, does not bind to a surface coated with Ni-NTA nor does it bind to a surface coated with DPgn. The likelihood is great that the enolase species on the surface of Streptococcus pyogenes is an x-mer of the native octamer.

biophysics↗

bioRxiv2018

Inexpensive, scalable camera system for tracking rats in large spaces

AbstractMost studies focused on understanding the neural circuits underlying spatial navigation are restricted to small behavioral arenas ([≤] 1 m2) because of the limits imposed by the cables extending from the animal to the recording system. New wireless recording systems have significantly increased the recording range. However, the size of arena is still constrained by the lack of a video tracking system capable of monitoring the animals movements over large areas integrated with these recording systems. We developed and benchmarked a novel, open-source, scalable multi-camera tracking system based on commercially available and low-cost hardware (Raspberry Pi computers and Raspberry Pi cameras). This Picamera system was used in combination with a wireless recording system for characterizing neural correlates of space in environments of various sizes up to 16.5 m2. Spatial rate maps generated using the Picamera system showed improved accuracy in estimating spatial firing characteristics of neurons compared to a popular commercial system, due to its better temporal accuracy. The system also showed improved accuracy in estimating head direction cell tuning as well as theta phase precession in place cells. This improved temporal accuracy is crucial for accurately aligning videos from multiple cameras in large spaces and characterizing spatially modulated cells in a large environment.

neuroscience↗