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Biology subjects

Goswami, N.

Publications and source records attributed to Goswami, N..

6 recordsLinked to original sources

Robust and accessible in situ sequencing with an open-source automated sequencer

Targeted in situ sequencing underlies many spatial transcriptomic, barcoded connectomic, and multiplexed perturbation techniques. Each experiment requires many cycles of chemical reactions and imaging that are time-consuming and error-prone to perform manually, and few commercial platforms can sequence highly diverse RNA barcodes in situ at cellular resolution across large tissue sections. Here we present Seq-o-Matic, an open-source automated in situ sequencer built entirely from commercially available components, which integrates fluidics, heated incubation, and imaging of up to three continuous 18 mm x 50 mm areas on standard microscope slides. Relative to manual sequencing, Seq-o-Matic reduced hands-on time 6- to 17.5-fold and total run time about 2.6-fold. Across three runs spanning two mouse strains and a neonatal marmoset, sequencing quality, measured by signal retention, channel specificity, and phasing, was similar to that of four manually sequenced mouse brains. In four mouse hemispheres sequenced in separate runs (two C57BL/6J, one NOD, and one WSB), consensus cell types were present in every brain and corresponded to those of a manually sequenced C57BL/6J brain. Within cortical excitatory neurons, the two C57BL/6J brains were more similar to each other than were brains of different strains, suggesting that technical variation between runs was small relative to strain differences. Seq-o-Matic thus makes consistent, high-throughput in situ sequencing accessible to laboratories without specialized in situ sequencing expertise.

genomics↗

Environmental gradients shape climate adaptive phenotypes in Muscovy ducks in Benin with implications for adaptation policy.

Climate change threatens livestock-dependent livelihoods across sub-Saharan Africa, yet National Adaptation Programmes of Action (NAPAs) and Nationally Determined Contributions (NDCs) rarely incorporate animal genetic resource management despite explicit mandates under the Convention on Biological Diversitys Global Plan of Action for Animal Genetic Resources. This study addresses this policy gap by investigating how environmental gradients shape phenotypic adaptation in Muscovy duck (Cairina moschata) populations across Benins three agroecological zones, generating evidence to inform climate-responsive livestock policy. We surveyed 505 farmers and characterized 1,300 ducks across dry (Temperature-Humidity Index [THI]: 70-74), sub-humid (THI: 74-78), and humid (THI: 78-82) zones. Climate analysis confirmed accelerating thermal stress (+0.38 degrees C per decade in humid zones), exceeding IPCC West African projections and threatening Sustainable Development Goal 2 (Zero Hunger) targets. Principal component analysis revealed zone-specific phenotypic clustering (57.9% variance explained), with thermoregulatory traits showing coordinated adaptation: body volume-surface ratios 42.7% lower in humid zones (p = 0.002), limb proportions 72.7% higher (p < 0.001), and white feather coverage 31.3% greater (p = 0.019). Reproductive performance declined significantly along the thermal gradient (lifetime production: 40.7 vs. 37.1 eggs; p < 0.001), quantifying climate-driven productivity losses relevant to national food security assessments. Phenotype-climate associations demonstrated that locally adapted variants (black-white pied in dry zones; white-black-green pied in humid zones) maintain superior fitness under native conditions, providing critical evidence for the breed-for-environment approach advocated by FAOs Climate-Smart Agriculture framework. These findings provide an empirical foundation for incorporating animal genetic resource conservation into Benins NDC implementation, ECOWAS regional livestock strategies, and the African Unions Comprehensive Africa Agriculture Development Programme (CAADP) climate resilience targets.

evolutionary biology↗

Machine learning assisted health viability assay for mouse embryos with artificial confocal microscopy (ACM)

The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report a machine-learning assisted embryo health assessment tool utilizing a quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.

bioengineering↗

Monitoring Reactivation of Latent HIV by Label-Free Gradient Light Interference Microscopy

Latent human immunodeficiency virus (HIV) reservoirs in infected individuals present the largest barrier to a cure. The first step towards overcoming this challenge is to understand the science behind latency-reactivation interplay. Fluorescence imaging of GFP-tagged HIV has been the main method for studying reactivation of latent HIV in individually infected cells. In this paper, we report insights provided by label-free, gradient light interference microscopy (GLIM) about the changes in measures including dry mass, diameter, and dry mass density associated with infected cells that occur upon reactivation. We discovered that mean cell dry mass and mean diameter of latently infected cells treated with reactivating drug, TNF-, are higher for cells with reactivated HIV as compared to those with latent disease. Results also indicate that cells with mean dry mass and diameter less than 10pg and 8{micro}m, respectively, remain exclusively in the latent state. Also, cells with mean dry mass greater than 23pg and mean diameter greater than 11{micro}m have a higher probability of reactivating. This study is significant as it presents a new label-free approach to quantify latent reactivation of a virus in single cells based on changes in cell morphology.

bioengineering↗

Rapid SARS-CoV-2 Detection and Classification Using Phase Imaging with Computational Specificity

Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical pathlength sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A), HAdV (adenovirus), and ZIKV (Zika). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically. One Sentence SummaryThis work proposes a rapid (<1 min.), label-free testing method for SARS-CoV-2 detection, using quantitative phase imaging and deep learning.

bioengineering↗

The Effects of Varying Gravito-inertial Stressors on Grip Strength and Hemodynamic Responses Across Gender

The body behaves as a global system with many interconnected subsystems. While the effects of a gravitational change on body responses have been extensively studied in isolation, we are not aware of any study that examined two types of body responses concurrently. Here, we examined how the neurocognitive and cardiovascular systems interact in this singular context and whether these combined responses are influenced by gender. Ten women and nine men underwent three 5-minute centrifugation sessions (2.4g at the feet, 1.5g at the heart) in which participants rhythmically moved a hand-held object for 20 seconds. Grip force and hemodynamic responses were continuously measured during centrifugation and rest periods. Our results show that men optimize the modulation between grip force and the destabilizing load force, but not women. Exposure to artificial gravity induced higher heart rate and mean arterial pressure in both genders compared to baseline. However, during exposure, only women decreased heart rate across sessions. Interestingly, we found that Finishers (N=13, mostly men) and Non-Finishers (N=6, mostly women) exhibited divergent patterns of hemodynamic responses. We also suggest that the lack of grip force adaptation reported in women can be linked to challenged hemodynamic responses in that population. Finally, by deriving a simple model to predict failure to complete the experiment, we found that mean arterial pressure was the most relevant dimension, and not gender. As artificial gravity is being proposed as a countermeasure in long-term manned missions, our results are particularly important but also deserve follow-up studies.

physiology↗