Biology subjects

Shah, K. H.

Publications and source records attributed to Shah, K. H..

4 recordsLinked to original sources

bioRxiv2026

LAS3R: A simple, secure, scalable, and robust framework fordeploying lab automation devices

Laboratory automation can greatly accelerate experiments and data collection, yet building automated systems often requires substantial programming and electronics expertise, and few frameworks are targeted at deploying many devices. We present LAS3R, a low-cost, open-source framework that enables researchers with minimal technical expertise to rapidly prototype, deploy, remotely control, and collect data from multiple custom-built laboratory devices while maintaining strong security and reliability throughout the process--from early prototyping to routine operation. The system is built around a central hub to which multiple lab devices connect. This hub can be set up on a Raspberry Pi (a small, low-cost single-board computer) in under fifteen minutes. In the setup process, code is automatically generated for ESP32 microcontroller boards that control the hardware. Users can choose from a list of preconfigured ESP32 devices, for example a bioreactor, or use a template that provides base code for many common automation tasks, which they can then easily customise using the beginner-friendly Arduino platform. The ESP32 devices connect through a secure Wi-Fi network hosted by the Raspberry Pi that encrypts communication, and ensures only authorised hardware can join, helping safeguard experimental data and institutional networks, even while prototyping. We demonstrate the framework with two applications--a turbidostat bioreactor and a light-level controller--and show that it can simultaneously manage eight devices with 24 sensors. Robustness was evaluated through single-point-of-failure analysis, confirming continued operation during mains power or network interruptions. Comprehensive documentation, aimed at wet lab researchers, enables users to understand, build, and adapt the system, making it both a practical laboratory automation platform, including for those in low-resource settings, and a teaching resource. This paper is intended to be a technical evaluation of the architecture. Those wishing to deploy the system should refer to the online documentation at kavihshah.github.io/LAS3R.

bioengineering↗

bioRxiv2025

BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection

1A standard task in the analysis of spatially resolved transcriptomics data is to identify spatially variable genes (SVGs). This is most commonly done within one tissue section at a time because the spatial relationships between the tissue sections are typically unknown. However, large-scale spatial atlases are being generated, for example across hundreds of donors, where the goal is to identify a common set of SVGs to use for downstream analyses. One challenge is how to identify and remove SVGs that are associated with a known bias or technical artifact, such as the slide or capture area, which can lead to poor performance in downstream analyses, such as spatial domain detection. Here, we introduce BatchSVG, a tool to identify batch-biased genes in the application of SVG detection. Our approach compares the rank of per-gene deviance under a binomial model (i) with and (ii) without including a covariate in the model that is associated with the known bias or technical artifact. If the rank of a gene changes significantly between these, then we infer that this gene is likely associated with the bias or technical artifact and should be removed from the downstream analysis. We consider two SRT datasets and show how our model can improve the results of downstream analysis.

genomics↗

bioRxiv2024

Addressing the mean-variance relationship in spatially resolved transcriptomics data with spoon

An important task in the analysis of spatially resolved transcriptomics data is to identify spatially variable genes (SVGs), or genes that vary in a 2D space. Current approaches rank SVGs based on either p-values or an effect size, such as the proportion of spatial variance. However, previous work in the analysis of RNA-sequencing identified a technical bias, referred to as the "mean-variance relationship", where highly expressed genes are more likely to have a higher variance. Here, we demonstrate the mean-variance relationship in spatial transcriptomics data. Furthermore, we propose spoon, a statistical framework using Empirical Bayes techniques to remove this bias, leading to more accurate prioritization of SVGs. We demonstrate the performance of spoon in both simulated and real spatial transcriptomics data. A software implementation of our method is available at https://bioconductor.org/packages/spoon.

genomics↗

bioRxiv2024

γ-aminobutyric acid receptor B signaling drives glioblastoma in females in an immune-dependent manner

Sex differences in immune responses impact cancer outcomes and treatment response, including in glioblastoma (GBM). However, host factors underlying sex specific immune-cancer interactions are poorly understood. Here, we identify the neurotransmitter {gamma}-aminobutyric acid (GABA) as a driver of GBM-promoting immune response in females. We demonstrated that GABA receptor B (GABBR) signaling enhances L-Arginine metabolism and nitric oxide synthase 2 (NOS2) expression in female granulocytic myeloid-derived suppressor cells (gMDSCs). GABBR agonist and GABA analog promoted GBM growth in females in an immune-dependent manner, while GABBR inhibition reduces gMDSC NOS2 production and extends survival only in females. Furthermore, female GBM patients have enriched GABA transcriptional signatures compared to males, and the use of GABA analogs in GBM patients is associated with worse short-term outcomes only in females. Collectively, these results highlight that GABA modulates anti-tumor immune response in a sex-specific manner, supporting future assessment of GABA pathway inhibitors as part of immunotherapy approaches.

cancer biology↗