Biology subjects

Hernandez, O.

Publications and source records attributed to Hernandez, O..

4 recordsLinked to original sources

bioRxiv2026

Multimodal alignments of in vivo imaging and spatial biology datasets at cellular resolution

Parallel revolutions in intravital microscopy and spatial biology techniques have respectively enabled large-scale recordings of cellular dynamics in live animals and multi-dimensional molecular profiling at single-cell resolution. However, due to the challenges of aligning data from different modalities at cellular resolution, these two transformational approaches have generally been applied on separate biological samples, stymying the ability to link activity patterns and molecular attributes in the same exact cells. To enable routine, multimodal investigations of cells in vivo dynamics and molecular content, we created TRU-FACT (Total Registration Under Functional Activity, Connectivity, and Transcriptomics), a broadly applicable experimental and computational pipeline for registering large populations of individual cells across intravital imaging and spatial biology datasets. The pipeline combines three key innovations: an optomechanical tissue handling and alignment method to parallelize specimen planes, a graph-theoretic method to register individual cells based on their geometric relationships to neighboring cells, and a statistical framework that provides for each cell an a posteriori probability of correct registration. We validated TRU-FACT with several preparations for imaging neural Ca2+ activity in cortical and deep brain areas in head-fixed and freely behaving mice, RNA-barcode-expressing viruses for labeling neural projections, and low- and high-plex spatial transcriptomic methods. In mice performing a skilled reaching task, TRU-FACT alignments revealed the movement-related signaling patterns of intratelencephalic, extratelencephalic, and striatum-, superior colliculus-, and thalamus-projecting motor cortical neurons. Overall, TRU-FACT constitutes a scalable, multimodal discovery platform that is applicable to diverse tissue-types and spatial biology techniques, thereby enabling multiscale analyses of many complex biological systems.

neuroscience↗

bioRxiv2024

ActSort: An active-learning accelerated cell sorting algorithm for large-scale calcium imaging datasets

Recent advances in calcium imaging enable simultaneous recordings of up to a million neurons in behaving animals, producing datasets of unprecedented scales. Although individual neurons and their activity traces can be extracted from these videos with automated algorithms, the results often require human curation to remove false positives, a laborious process called cell sorting. To address this challenge, we introduce ActSort, an active-learning algorithm for sorting large-scale datasets that integrates features engineered by domain experts together with data formats with minimal memory requirements. By strategically bringing outlier cell candidates near the decision boundary up for annotation, ActSort reduces human labor to about 1-3% of cell candidates and improves curation accuracy by mitigating annotator bias. To facilitate the algorithms widespread adoption among experimental neuroscientists, we created a user-friendly software and conducted a first-of-its-kind benchmarking study involving about 160,000 annotations. Our tests validated ActSorts performance across different experimental conditions and datasets from multiple animals. Overall, ActSort addresses a crucial bottleneck in processing large-scale calcium videos of neural activity and thereby facilitates systems neuroscience experiments at previously inaccessible scales.

neuroscience↗

bioRxiv2021

Fast and statistically robust cell extraction from large-scale neural calcium imaging datasets

State-of-the-art Ca2+ imaging studies that monitor large-scale neural dynamics can produce video datasets that tally up to [~]100 TB in size ([~]10 days transfer over 1 Gbit/s ethernet). Processing such data volumes requires automated, general-purpose and fast computational methods for cell identification that are robust to a wide variety of noise sources. We present EXTRACT, an algorithm that is based on robust estimation theory and uses graphical processing units (GPUs) to extract neural dynamics from a typical Ca2+ video in computing times up to [~]10-times faster than imaging durations. We extensively validated EXTRACT on simulated and experimental data and processed 199 public datasets ([~]12 TB) from the Allen Institute in a day. Showcasing its superiority over past cell extraction methods at removing noise contaminants, neural activity traces from EXTRACT allow more accurate decoding of animal behavior. Overall, EXTRACT is a powerful computational tool matched to the present challenges of neural Ca2+ imaging studies in behaving animals.

neuroscience↗

bioRxiv2020

Paracoccidioides genomes reflect high levels of species divergence and little interspecific gene flow

The fungus Paracoccidioides spp. is a prevalent human pathogen endemic to South America. The genus is composed of five species. In this report, we use 37 whole genome sequences to study the allocation of genetic variation in Paracoccidioides. We tested three genome-wide predictions of advanced speciation, namely, that all species should be reciprocally monophyletic, that species pairs should be highly differentiated along the whole genome, and that there should be low rates of interspecific gene exchange. We find support for these three hypotheses. Species pairs with older divergences show no evidence of gene exchange, while more recently diverged species pairs show evidence of modest rates of introgression. Our results indicate that as divergence progresses, species boundaries become less porous among Paracoccidioides species. Our results suggest that species in Paracoccidioides are at different stages along the divergence continuum. IMPORTANCEParacoccidioides is the causal agent of the most frequent systemic mycosis in Latin America. Most of the inference of the evolutionary history of Paracoccidioides has used only a handful of molecular markers. In this report, we evaluate the extent of genome divergence among Paracoccidioides species and study the possibility of interspecific gene exchange. We find that all species are highly differentiated. We also find that the amount of gene flow between species is low and in some cases even completely absent in spite of geographic overlap. Our study constitutes a systematic effort to identify species boundaries in fungal pathogens, and determine the extent of gene exchange among fungal species.

microbiology↗