Biology subjects

Sanders, L. M.

Publications and source records attributed to Sanders, L. M..

6 recordsLinked to original sources

bioRxiv2026

Automated behavioral segmentation and markerless pose tracking of mice during spaceflight

The NASA Rodent Habitat aboard the International Space Station enabled long-duration studies of behavioral responses to spaceflight, but video-based behavioral analysis has relied on laborious manual annotation. No study has tested whether deep learning tools can automate this analysis under the demanding imaging conditions of orbital vivaria. We applied pose estimation (SLEAP) and behavioral segmentation (DeepEthogram) to archival footage from the Rodent Research-1 mission. Nine labelers annotated 3,249 pose labels across 2,063 frames, and three behaviorists labeled 411,194 frames across 66 videos. Pose tracking accuracy approximated human inter-annotator variability despite progressive lens soiling, grid occlusions, and spherical aberration. Behavioral classification across eight categories achieved accuracy of 0.86-0.90 and suggests progressive behavioral adaptations to microgravity. Kinematic reconstruction of circling estimated centripetal accelerations periodically approaching 1g. This is the first application of deep learning-based pose estimation and behavioral segmentation to rodents in spaceflight, establishing benchmarks for future monitoring systems. One-Sentence SummaryDeep learning-based pose estimation and behavioral segmentation of ISS video reveal progressive behavioral adaptations of mice to microgravity

neuroscience↗

bioRxiv2026

Cross-Species Adaptation of RETFound for Rodent OCT Age Estimation Reveals Strong CNN Baselines in Data-Scarce Space Biology

Space-biology imaging studies are often constrained by severe data scarcity, limiting the development of robust machine-learning biomarkers. Rodent spaceflight and space-analog datasets provide an important preclinical setting for testing transfer-learning strategies, but the extent to which human retinal foundation models can generalize to rodent optical coherence tomography (OCT) remains unclear. Here, we benchmark cross-species adaptation of RETFound, a human retinal Vision Transformer pretrained on 1.6 million retinal images, for chronological age prediction from Brown Norway rat OCT B-scans in the NASA Open Science Data Repository dataset OSD-679. We adapted RETFound using Low-Rank Adaptation (LoRA) and evaluated performance on control animals under matched 3-fold rat-level cross-validation. We compared RETFound+LoRA with a strong ImageNet-pretrained Xception baseline under matched protocols and included a scratch/random ViT as a supplementary negative-control architecture check. Metrics included mean absolute error (MAE), R2, and inter-eye mean absolute difference (MAD). RETFound+LoRA achieved MAE = 26.20 {+/-} 5.03 days with R2 = 0.744 {+/-} 0.049. However, Xception performed better in the primary benchmark (MAE = 19.01 {+/-} 7.67 days, R2 = 0.853 {+/-} 0.082), and the matched-fold comparison favored Xception, although this result should be interpreted cautiously given the small number of folds. Inter-eye consistency was maintained across the matched control evaluation, and saliency maps localized model attention to anatomically plausible inner retinal regions. Together, these results show that human retinal foundation models can transfer to rodent OCT in a scientifically useful way, but also that strong CNN baselines may outperform transformer-based models in small-sample cross-species settings. This preprint provides a reproducible benchmark and baseline framework for future retinal biomarker development in space biology. Significance StatementSpace-biology imaging studies are intrinsically data-limited. This preprint provides a reproducible cross-species benchmark for adapting Earth-trained retinal models to rodent OCT under small-sample conditions, highlights the value of strong CNN baselines, and offers a reusable starting point for future retinal biomarker development in space-relevant datasets.

bioinformatics↗

bioRxiv2025

Machine learning ensemble identifies distinct age-related response to spaceflight in mammary tissue

BackgroundSpaceflight presents unique environmental stressors, such as microgravity and radiation, that significantly affect biological systems at the molecular, cellular, and organismal levels. Astronauts face an increased risk of developing cancer due to exposure to ionizing radiation and other spaceflight-related factors. Age plays a crucial role in the bodys response to the cellular stresses that lead to cancer, with younger organisms generally exhibiting more efficient response mechanisms than older ones. The vast majority of research investigating breast cancer risk from spaceflight is done in vitro, using cell lines exposed to simulated radiation and microgravity. ObjectivesThe primary objective of this observational study is to characterize the response to spaceflight of in vivo murine mammary tissue and identify the molecular biomarkers enriched in this response using mice flown on the International Space Station. The secondary objective is to determine if age plays a role in this response. MethodsThe NASA Open Science Data Repository (OSDR) has curated transcriptomic data obtained from murine mammary tissue in a controlled experiment (OSD-511) which includes 43 young and old female mice. In this study, we utilized an ensemble of four machine learning binary classifiers (logistic regression, support vector machine, random forest, and single-layer perceptron) to analyze gene expression profiles to predict age (old vs young) and condition (spaceflight vs ground control). Using the genes our ensemble identified as most predictive, we performed pathway enrichment analysis to investigate the molecular pathways involved in spaceflight-related health risks, particularly in the context of breast cancer. ResultsAll space-flown mice responded to spaceflight with evidence of systemic metabolic reprogramming and mitochondrial adaptation to microgravity and radiation as compared to their 33 ground control counterparts. For the 10 mice flown in space, older mice exhibited chronic, significantly enriched pathways related to cell adhesion and extracellular matrix (ECM) structure, while younger mice showed acute activation of pathways involved in cortisol synthesis and adrenergic stress response (with false discovery rate adjusted q-values < .001). These results provide putative biomarkers for monitoring and treating breast cancer. ConclusionsThese findings highlight the critical role of age in modulating the response to spaceflight-induced stress and suggest that these molecular pathways may contribute to differential outcomes in tissue homeostasis, metabolic disorders, and breast cancer tumorigenesis. Moreover, our computational methodology may be applied to several unexplored datasets in OSDR and beyond.

bioinformatics↗

bioRxiv2025

Foundational Architecture Enabling Federated Learning for Training Space Biomedical Machine Learning Models between the International Space Station and Earth

The public and commercial space industries are planning longer duration and more distant space missions, including the establishment of a habitable lunar base and crewed missions to Mars. To support Earth-independent scientific and medical operations, such missions can leverage artificial intelligence and machine learning models to assist with crew healthcare, spacecraft maintenance, and other critical tasks. However, transferring large volumes of data between Earth and space for model development consumes valuable bandwidth, is vulnerable to communication disruptions, and may compromise crew safety and data privacy. Federated learning enables model training while keeping data in situ and only transferring model parameters. In this work, we present a flexible, resilient federated learning framework that provides the secure transmission of model updates between Earth and the International Space Station. On March 15, 2024, this framework pioneered the deployment of federated learning in a spaceflight setting, training classifier models between Earth and the ISS using both real biomedical research data and synthetically generated data.

bioinformatics↗

bioRxiv2024

A Machine Learning Model of Perturb-Seq Data for Use in Space Flight Gene Expression Profile Analysis

The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects but does not necessarily indicate the initial point of interference within a network. The objective of this project is to take advantage of large scale and genome-wide perturbational or Perturb-Seq datasets by using them to pre-train a generalist machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of single cell RNA sequencing data collected from CRISPR knock out screens in cell culture. The advent of generative machine learning algorithms, particularly transformers, make it an ideal time to re-assess large scale data libraries in order to grasp cell and even organism-wide genomic expression motifs. By tailoring an algorithm to learn the downstream effects of the genetic perturbations, we present a pre-trained generalist model capable of predicting the effects of multiple perturbations in combination, locating points of origin for perturbation in new datasets, predicting the effects of known perturbations in new datasets, and annotation of large-scale network motifs. We demonstrate the utility of this model by identifying key perturbational signatures in RNA sequencing data from spaceflown biological samples from the NASA Open Science Data Repository.

bioinformatics↗

bioRxiv2024

Systemic Genome Correlation Loss as a Central Characteristic of Spaceflight

Spaceflight exposes the human body to a unique combination of stressors--microgravity, radiation, and confinement--that induces multisystemic physiological dysregulation. Traditional transcriptomic analyses have focused on differential expression to identify key genes, yet this approach fails to explain why astronauts experience systemic fragility despite often subtle changes in gene abundance. Here, we present a comprehensive meta-analysis of 10 independent transcriptomic and genomic datasets (N = 136) from the NASA Open Science Data Repository. By shifting focus from gene abundance to gene-gene correlation topology, we identify Systemic Genome Correlation Loss as a central biosignature of spaceflight. We show that the regulatory architecture of the transcriptome undergoes a profound decoherence in microgravity, shifting the global correlation distribution toward stochasticity (p < 10-15). This phenomenon is universal across tissues and independent of gene variance. We identify a massive population of 760 genes that maintain stable expression levels but lose over 500 regulatory connections each, outnumbering canonical differentially expressed genes by three-to-one. Finally, we demonstrate that cells preserve the connectivity of survival-critical DNA repair networks preferentially while allowing mitochondrial and synaptic networks to shatter. These findings suggest that astronaut health risks are driven by the entropic decay of regulatory synchronization, proposing a new paradigm for countermeasure development focused on network stabilization rather than pathway inhibition.

genomics↗