Biology subjects

Lehman, M.

Publications and source records attributed to Lehman, M..

6 recordsLinked to original sources

bioRxiv2024

Computer vision guided open-source active commutator for neural imaging in freely behaving animals

Recently developed miniaturized neural recording devices that can monitor and perturb neural activity in freely behaving animals have significantly expanded our knowledge neural underpinning of complex behaviors. Most miniaturized neural interfaces require a wired connection for external power and data acquisition systems. The wires are required to be commutated through a slip ring to accommodate for twisting of the wire or tether and alleviate torsional stresses. The increased trend towards long term continuous neural recordings have spurred efforts to realize active commutators that can sense the torsional stress and actively rotation the slip ring to alleviate torsional stresses. Current solutions however require addition of sensing modules. Here we report on an active translating commutator that uses computer vision (CV) algorithms on behavioral imaging videos captured during the experiment to track the animals position and heading direction in real-time and uses this information to control the translation and rotation of a slipring commutator to accommodate for accumulated mouse heading orientation changes and position. The CV guided active commutator has been extensively tested in three separate behavioral contexts and we show reliable cortex-wide imaging in a mouse in an open-field with a miniaturized widefield cortical imaging device. Active commutation resulted in no changes to measured neurophysiological signals. The active commutator is fully open source, can be assembled using readily available off-the-shelf components, and is compatible with a wide variety of miniaturized neurophotonic and neurophysiology devices.

neuroscience↗

bioRxiv2024

Valine Catabolism Drives Bioenergetic and Lipogenic Fuel Plasticity in Prostate Cancer

Metabolic reprogramming is a hallmark of cancer and fundamental for disease progression. The remodelling of oxidative phosphorylation and enhanced lipogenesis are key characteristics of prostate cancer (PCa). Recently, succinate-dependent mitochondrial reprogramming was identified in high-grade prostate tumours with upregulation of enzymes associated with branched-chain amino acid (BCAA) catabolism. We hypothesised that the degradation of BCAAs, particularly valine may play a critical role in anapleurotic refuelling of the mitochondrial succinate pool. Through suppression of valine availability, we report strongly reduced lipid content despite compensatory upregulation of fatty acid uptake, indicating valine is an important lipogenic fuel in PCa. Inhibition of the enzyme 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) also resulted in selective inhibition of cellular proliferation of malignant but not benign prostate cells and impaired succinate production. In combination with a comprehensive multi-omic investigation of patient and cell line data, our work highlights a therapeutic target for selective inhibition of metabolic reprogramming in PCa.

cancer biology↗

bioRxiv2023

Neural circuits underlying context-dependent competition between defensive actions in Drosophila larva

To ensure their survival, animals must be able to respond adaptively to threats within their environment. However, the precise neural circuit mechanisms that underlie such flexible defensive behaviors remain poorly understood. Using neuronal manipulations, machine-learning-based behavioral detection, Electron Microscopy (EM) connectomics and calcium imaging in Drosophila larva, we have mapped the second-order interneurons differentially involved in the competition between different defensive actions and the main pathways to the motor side putatively involved in inhibiting startle-type behaviors and promoting escape behaviors in a context dependent manner. We found that mechanosensory stimulation modulates the nociceptive escape sequences and inhibits C-shape bends and Rolls in favor of startle-like behaviors. This suggests a competition between mechanosensory-induced startle responses and escape behaviors. Structural and functional connectivity revealed that the second order interneurons receive their main input from projection neurons that integrate mechanosensory and nociceptive stimuli. The analysis of their postsynaptic connectivity in EM revealed that they make indirect connections to the pre-motor and motor neurons. Finally, we identify a pair of descending neurons that could promote modulate the escape sequence and promote startle behaviors. Altogether, these results characterize the pathways involved in the Startle and Escape competition, modulated by the sensory context.

neuroscience↗

bioRxiv2023

Unsaturated intercellular vapor pressure is relevant for leaf water heavy isotope enrichment

Leaf intercellular vapor pressure (ei) can be unsaturated, but its effect on leaf water heavy isotope enrichment (LWE) has not yet been quantified. We evaluated the ecological relevance of unsaturated ei for LWE, i.e., for leaf water oxygen-18 and deuterium enrichment, using data from a boreal forest stand and a large-scale dataset. Unsaturated ei can firstly affect LWE by directly decreasing ei in the Craig Gordon model (Mechanism 1), which leads to an increased influence of atmospheric vapor isotopic enrichment above source water ({Delta}v), and a decreased influence of kinetic fractionation by diffusion through the stomata and boundary layer ({varepsilon}k). Unsaturated ei can secondly affect LWE by changing {varepsilon}k (Mechanism 2). To evaluate the effect of Mechanism 1 to LWE, we employed sensitivity tests on LWE model performance using varying measured intercellular relative humidity (RHcellular), or RHcellular fitted to observed LWE. To explore the effects of Mechanism 2 to LWE, we modified the calculation of {varepsilon}k and observed consequences to LWE predictions. Unsaturated ei is relevant to LWE by Mechanism 1, since a lowered RHcellular noticeably changed LWE predictions. It clearly improved deuterium predictions and conditionally improved oxygen-18 predictions. Isotope fractionation by Mechanism 2 is unlikely relevant to oxygen-18 and deuterium enrichment. Unsaturated ei must now be recognized as a variable that introduces error to heavy isotope enrichment models and reconstructions from organic material, via Mechanism 1. We suggest a correction for unsaturated ei for both oxygen-18 and deuterium enrichment using a variable RHcellular calculated from atmospheric relative humidity.

plant biology↗

bioRxiv2022

Latent representation of single-cell transcriptomes enables algebraic operations on cellular phenotypes

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For tumor cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR-/NElow (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.

bioinformatics↗

bioRxiv2021

Gene expression based inference of drug resistance in cancer

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

cancer biology↗