Biology subjects

Franco, L. M.

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

9 recordsLinked to original sources

bioRxiv2026

Visual uncertainty and task demands shape active sensing strategies in mice

In natural environments, animals actively sample visual information to guide behavior. Sensory feedback is dynamic and often requires active movements, whether saccading across the lines of this page or walking through a park. From high-acuity vision in hawks to low-acuity mice, many animals actively navigate to seek information, which can be called infotaxis. Although mice have relatively low-acuity vision, they still rely on sight for critical behaviors including navigation and prey capture. Yet, how sensitive they are to visual information and performing infotaxis has not been established. Here, we develop a virtual reality object discrimination task to investigate visual decision-making under naturalistic conditions. We show that mice perform infotaxis by actively seeking out informative views to guide their choices. Stimulus manipulations confirm that this strategy is modulated by the amount of available visual information. These results reveal that mice use principled active strategies to resolve visual uncertainty, highlighting a key role for information-seeking in natural vision. HighlightsO_LIMice perform active visual sensing in a free-range VR object discrimination task. C_LIO_LIThey perform infotaxis and select the correct object under variable visibility. C_LIO_LIDecisions decoded from head-body movements and speed reflect occlusion sensitivity. C_LI

neuroscience↗

bioRxiv2025

Glucocorticoids regulate the human non-coding genome

Glucocorticoids (GCs) are widely used anti-inflammatory and immunosuppressive agents known to induce dramatic changes in gene expression, yet their effects on non-coding RNAs such as long non-coding RNAs (lncRNAs) or microRNAs (miRNAs) remain poorly characterized. We present the first comprehensive and systematic analysis of GC regulation of the non-coding genome across nine human primary cell types from both hematopoietic and non-hematopoietic lineages. In vitro GC treatment was applied to each cell type, with transcriptomic profiling (total RNA-seq and small RNA-seq) at 2 and 6 hours post-treatment. We identified over 2,000 GC-responsive non-coding transcripts, including 654 annotated lncRNAs, 1,376 novel lncRNAs, and 39 miRNAs. The non-coding RNA response to GCs was highly cell type-dependent: 80% of GC-responsive lncRNAs and 97% of miRNAs were unique to a single cell type. Hematopoietic cells exhibited a greater magnitude of lncRNA induction than non-hematopoietic cells. Notably, dozens of facultative lncRNAs (undetectable at baseline) were induced de novo by GC. GC-responsive transcripts spanned diverse lncRNA classes, with enrichment of host lncRNAs. By contrast, only a limited number of miRNAs were GC-responsive. GC regulation of transcript abundance for miRNAs and their host lncRNAs appears to be independent. Our results, which are accessible through an interactive web application, establish a framework for studying how non-coding transcription contributes to physiological and clinical heterogeneity in GC responses. The newly identified GC-responsive non-coding transcripts could represent biomarkers of GC exposure, determinants of GC sensitivity or resistance, or candidate regulators of tissue-specific GC effects.

genomics↗

bioRxiv2022

Differential Stability of Task Variable Representations in Retrosplenial Cortex

Cortical neurons store information across different timescales, from seconds to years. Although information stability is variable across regions, it can vary within a region as well. Association areas are known to multiplex behaviorally relevant variables, but the stability of their representations is not well understood. Here, we longitudinally recorded the activity of neuronal populations in the retrosplenial cortex (RSC) during the performance of a context-choice association task. We found that the activity of neurons exhibits different levels of stability across days. Using linear classifiers, we quantified the stability of three task-relevant variables. We find that RSC representations of context and trial outcome display higher stability than motor choice, both at the single cell and population levels. Together, our findings show an important characteristic of association areas, where diverse streams of information are stored with varying levels of stability, which may balance representational reliability and flexibility according to behavioral demands.

neuroscience↗

bioRxiv2022

Single-cell analysis reveals the range of transcriptional states of circulating human neutrophils

Neutrophils are the most abundant leukocytes in human blood and are essential components of innate immunity. Until recently, neutrophils were considered homogeneous and transcriptionally inactive cells, but both concepts are being challenged. To date, neutrophils have been characterized based on discrete parameters including cell-surface markers, buoyancy, maturation status, or tissue localization. Single-cell RNA sequencing (scRNA-seq) offers an unbiased view of cells along a continuum of transcriptional states. However, the use of scRNA-seq to characterize neutrophils has proven technically difficult, explaining in part the paucity of published single-cell data on neutrophils. We have found that modifications to the data analysis pipeline, rather than to the existing scRNA-seq chemistries, can significantly increase the detection of human neutrophils in scRNA-seq. We have then applied a modified pipeline to the study of human peripheral blood neutrophils. Our findings indicate that circulating human neutrophils are transcriptionally heterogeneous cells, which can be classified into one of four transcriptional clusters that are reproducible among healthy human subjects. We demonstrate that peripheral blood neutrophils shift from relatively immature (Nh0) cells, through a transitional phenotype (Nh1), into one of two endpoints defined by either relative transcriptional inactivity (Nh2) or high expression of type I interferon-inducible genes (Nh3). Transitions among states are characterized by the expression of specific transcription factors. By simultaneously measuring surface proteins and intracellular transcripts at the single-cell level, we show that these transcriptional subsets are independent of the canonical surface proteins that are commonly used to define and characterize human neutrophils. These findings provide a new view of human neutrophil heterogeneity, with potential implications for the characterization of neutrophils in health and disease.

immunology↗

bioRxiv2021

Experience-dependent plasticity modulates ongoing activity in the antennal lobe and enhance odor representations

Ongoing neural activity has been observed across several brain regions and thought to reflect the internal state of the brain. Yet, it is not fully understood how ongoing brain activity interacts with sensory experience and shape sensory representations. Here, we show that projection neurons of the fruit fly antennal lobe exhibit spatiotemporally organized ongoing activity in the absence of odor stimulation. Upon repeated exposure to odors, we observe a gradual and long-lasting decrease in the amplitude and frequency of spontaneous calcium events, as well as a reorganization of correlations between olfactory glomeruli during ongoing activity. Accompanying these plastic changes, we find that repeated odor experience reduces trial-to-trial variability and enhances the specificity of odor representations. Our results reveal a previously undescribed experience-dependent plasticity of ongoing and sensory driven activity at peripheral levels of the fruit fly olfactory system.

neuroscience↗

bioRxiv2020

A distributed circuit for associating environmental context to motor choice in retrosplenial cortex

During navigation, animals often use recognition of familiar environmental contexts to guide motor action selection. The retrosplenial cortex (RSC) receives inputs from both visual cortex and subcortical regions required for spatial memory, and projects to motor planning regions. However, it is not known whether RSC is important for associating familiar environmental contexts with specific motor actions. Here, we test this possibility by developing a task in which trajectories are chosen based on the context. We find that mice exhibit differential pre-decision activity in RSC, and that optogenetic suppression of RSC activity impairs task performance. Individual RSC neurons encode a range of task variables, often multiplexed with distinct temporal profiles. However, the responses are spatiotemporally organized, with task variables represented along a posterior-to-anterior gradient along RSC during the behavioral performance, consistent with histological characterization. These results reveal an anatomically-organized retrosplenial cortical circuit for associating environmental contexts to appropriate motor outputs.

neuroscience↗

bioRxiv2020

High-throughput imaging of mRNA at the single-cell level in human primary immune cells

Measurement of gene expression at the single-cell level has led to important advances in the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell gene expression approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for High-Content Imaging (HCI)-based quantification of transcript abundance at the single-cell level in primary human immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, which we abbreviate as hcHCR, combines the high sensitivity of the hybridization chain reaction (HCR) for the visualization of mRNA molecules in single cells, with the speed, scalability, and technical reproducibility of HCI. We first tested eight microscopy-compatible attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in a 384-well format and documented the ability of the method to detect increased or decreased transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of two transcripts per cell, both at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be a suitable and cost-effective assay for low- to medium-throughput chemical, genetic or functional genomic screens in primary human cells, with the possibility of performing personalized screens or screens on cells obtained from patients with a specific disease.

genomics↗

bioRxiv2020

Blood gene expression-based prediction of lethality after respiratory infection by influenza A virus in mice

Lethality after respiratory infection with influenza A virus (IAV) is associated with potent immune activation and lung tissue damage. In a well-controlled animal model of infection, we sought to determine if one could predict lethality using transcriptional information obtained from whole blood early after influenza virus exposure. We started with publicly available transcriptomic data from the lung, which is the primary site of the infection and pathology, to derive a multigene transcriptional signature of death reflective of innate inflammation associated with tissue damage. We refined this affected tissue signature with data from infected mouse and human blood to develop and validate a machine learning model that can robustly predict survival in mice after IAV challenge using data obtained from as little as 10 l of blood from early time points post infection. Furthermore, in genetically identical, cohoused mice infected with the same viral bolus, the same model can predict the lethality of individual animals but, intriguingly, only within a specific time window that overlapped with the early effector phase of adaptive immunity. These findings raise the possibility of predicting disease outcome in respiratory virus infections with blood transcriptional data and pave the way for translating such approaches to humans.

immunology↗

bioRxiv2020

A Deep Learning Pipeline for Nucleus Segmentation

Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size and pre-processing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pre-trained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning-based biological image segmentation using small annotated image datasets.

cell biology↗