Biology subjects

Pereira, T. D.

Publications and source records attributed to Pereira, T. D..

12 recordsLinked to original sources

bioRxiv2026

Spatial imprints of emergent cardiomyocyte states in the pressure-overloaded heart

Resilience to cardiac stress is essential for health, yet the relationship between cardiomyocyte (CM) stress response and local microenvironment remains unclear. Here, we combined MERFISH spatial transcriptome profiling with Cellouette, an improved cell segmentation method, to determine CM-microenvironment relationships in a mouse model of ventricular pressure overload. We report the shape, transcription profile, spatial organization, and physical connectivity for >400,000 cells across stressed and healthy tissues. Under stress, CMs adopted a spectrum of emergent transcriptional states, with advanced states marked by a metabolic and pro-fibrotic shift. To discover CM-environment relationships, we performed a network analysis of physical cell connectivity combined with cell-type-specific profiling. We found that pro-fibrotic CM progression was tightly linked to distinct local microenvironments, and CM metabolic shifts could be inferred from transcriptional patterns in neighboring non-CM cells, revealing microenvironmental imprints of disease. We thus provide a resource for understanding the heterogeneity of outcome during cardiac pressure overload. HighlightsO_LICellouette provides accurate segmentation for single-cell spatial transcriptomics in cardiac tissue. C_LIO_LIPressure overload creates spatial gradients of cardiomyocyte pro-fibrotic states. C_LIO_LICardiomyocyte pro-fibrotic progression is linked to changes in local cell composition and gene expression. C_LIO_LITranscriptional states of non-muscle cells predict metabolic state of adjacent cardiomyocytes. C_LI

genomics↗

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↗

bioRxiv2025

Connectome Similarity Varies with Global Metastability and Movie Content

IntroductionRecent evidence supports that non-linear segments of brain activity carries most of the individual-specific information which allows unique identification based on functional connectomes (e.g. connectome fingerprints) in MEG. In fMRI, metastability has emerged as a reliable and robust summary measure of brain dynamics. Here, we set out to test the hypothesis that metastability is associated with connectome fingerprinting in naturalistic fMRI. We further examined whether this putative association varies with time scales, across brain functional networks, and depending on the movie content. MethodsWe analyzed fMRI data from 86 participants of the Naturalistic Neuroimaging Database. Data was collected during movie-watching of 10 entire movies of different genres. Global and network-level metastability were calculated using an fMRI-appropriate proxy measure based on the variability of spatial coherence. The within-subject stability (Iself) and between-subjects similarity (Iothers) of individual functional connectomes were also calculated. Linear mixed-effects models and appropriate contrasts were used to test associations between metastability and fingerprinting indices. ResultsAt the whole-brain level and considering data from all movies, an association across windows was found between metastability, Iself, and Iothers, with stronger effects observed for larger windows, such as the 600-second window (Iself: b = -0.13, SE = 0.032, p < 0.001; Iothers: b = -0.23, SE = 0.028, p < 0.001). Associations were also observed with distinct magnitudes within the eight large-scale functional networks, with particularly strong effects in the Motor Network (Iself: b = -0.34, SE = 0.027, p < 0.001; Iothers: b = -0.27, SE = 0.021, p < 0.001). Although no whole-brain differences were observed, movie content exhibited distinct association patterns within networks, notably for the prediction of Iself in the Visual Network for 500 Days of Summer ({Delta} = -0.22, SE = 0.11, p = 0.043). For Iothers, significant between-movie differences were found in the AMN ({Delta} = -0.25, SE = 0.062, p < 0.001), Dorsal Attention ({Delta} = 0.16, SE = 0.08, p = 0.038), Ventral Attention ({Delta} = 0.14, SE = 0.060, p = 0.017), and Visual Networks ({Delta} = -0.17, SE = 0.081, p = 0.031). ConclusionMetastability is systematically related to functional connectome fingerprints. This relationship is time-scale dependent, varies across functional networks, and is sensitive to movie content. Our findings bridge nonlinear brain dynamics and connectome identifiability, highlighting naturalistic paradigms as a powerful framework for individualized precision neuroimaging.

neuroscience↗

bioRxiv2025

Asymmetric Social Representations in the Prefrontal Cortex for Cooperative Behavior

Cooperation is a hallmark of social species, enabling individuals to achieve goals that are unattainable alone. Across species, cooperative behaviors are often organized by distinct social roles such as leaders and followers, yet the neural mechanisms supporting such role-based coordination remain elusive. Here we introduce a new paradigm for studying cooperation in mice, where pairs of animals engage in a joint spatial foraging task that naturally gives rise to stable leader-follower roles predictive of learning speed. Disruption of medial prefrontal cortex (mPFC) activity, particularly in followers, impairs cooperation and induces reciprocal shifts in how animals weigh self- and partner-related cues for decision-making. Calcium imaging reveals that mPFC encodes both leadership dynamics and an egocentric social value map of the partners position, each in an asymmetric, role-specific manner. Combining this behavior with a novel multi-agent inverse reinforcement learning framework, we identify latent value functions that guide cooperative decisions and are decodable from mPFC activity. These findings uncover fundamental neural computations that support cooperation, revealing how social roles shape decision-making in real time. Our work opens new avenues for investigating the cellular and circuit basis of social cognition and collective behavior.

neuroscience↗

bioRxiv2025

Social Exclusion Amplifies Behavioral Responses to Physical Pain via Insular Neuromodulation

The "Pain Overlap Theory" (1) proposes that the experience of social pain overlaps with and amplifies the experience of physical pain by sharing parts of the same underlying processing systems (2-6). In humans, the insular cortex has been implicated in this overlap of physical and social pain, but a mechanistic link has not been made (2,4,5,7-9). To determine whether social pain can subsequently impact responses to nociceptive stimuli via convergent electrical signals (spikes) or convergent chemical signals (neuromodulators), we designed a novel Social Exclusion paradigm termed the Fear of Missing Out (FOMO) Task which facilitates a mechanistic investigation in mice. We found that socially-excluded mice display more severe responses to physical pain, disrupted valence encoding, and impaired neural representations of nociceptive stimuli. We performed a systematic biosensor panel and found that endocannabinoid and oxytocin signaling in the insular cortex have opposing responses during trials where mice were attending or not attending to the Social Exclusion events respectively, demonstrating distinct neuromodulatory substrates that underpin different states of Social Exclusion. We also found that intra-insular blockade of oxytocin signaling increased the response to physical pain following Social Exclusion. Together these findings suggest Social Exclusion effectively alters physical pain perception using neuromodulatory signaling in the insular cortex.

neuroscience↗

bioRxiv2024

Unc-51-like Kinase 1 (ULK1) Regulates Bacterial Ubiquitylation and p62 Recruitment during Xenophagic Clearance of Listeria monocytogenes

Autophagy is an essential cellular homeostatic process, that also serves as an innate immune mechanism against intracellular bacterial pathogens, through a highly selective form of autophagy known as xenophagy. Despite advances in understanding how bacteria are targeted for autophagic degradation, the specific regulatory mechanisms that drive the initial steps and ensure bacterial selection remain incompletely defined. Our study uncovers a pivotal role for Unc-51-like kinase 1 (ULK1) in the xenophagic clearance of the intracellular bacterial pathogen Listeria monocytogenes. We observed that ULK1 is essential for the efficient ubiquitylation of bacteria and subsequent recruitment of the autophagic adaptor protein p62 to the bacterial surface. Furthermore, we show that the impact of ULK1 deficiency in these early events - reduction in bacterial ubiquitylation followed by impaired p62 targeting - later result in diminished formation of bacteria-targeted autophagosomes. Notably, phosphorylation of p62 at the S409 residue, which is known to enhance its affinity for ubiquitin, is necessary for its recruitment and effective bacterial clearance, highlighting the regulatory role of ULK1 in this process. These findings unveil a previously unrecognized function of ULK1 in modulating early xenophagy steps, contributing to the autophagic control of intracellular pathogens. Our findings offer new perspectives into the manipulation of ULK1activity for therapeutic interventions against infectious diseases. IMPORTANCEAutophagy is a vital process in eukaryotic cells that enables them to digest intracellular components, helping them respond to various stresses, including starvation, the accumulation of dysfunctional organelles, and infections. While the autophagic flux has been extensively studied over the past few decades, some key mechanisms remain poorly understood. In our research, we aimed to clarify one such mechanism: the way the autophagic machinery specifically targets intracellular bacteria. We identified a novel role for the protein ULK1 in this process, demonstrating that ULK1 is essential for marking bacteria within the cell and recruiting the protein p62. This marking and recruitment of p62 are critical steps for effective bacterial clearance. This underscores the pivotal role of ULK1 in initiating the cellular defense against bacterial infections. Our findings could pave the way for new therapeutic strategies aimed at enhancing the bodys capacity to combat bacterial infections.

cell biology↗

bioRxiv2024

Deep Imputation for Skeleton Data (DISK) for Behavioral Science

AbstractPose estimation methods and motion capture systems have opened doors to quan- titative measurements of animal kinematics. However, these methods are not perfect and contain missing data. Our method, Deep Imputation for Skeleton data (DISK), leverages deep learning algorithms to learn dependencies between keypoints and their dynamics to impute missing tracking data. We developed an unsupervised training scheme, which does not rely on manual annotations, and tested several neural network architectures for the imputation task. We found that transformer outperforms other architectures including graph con- volutional networks that were developed specifically for skeleton-based action recognition. We demonstrate the usability and performance of our imputation method on seven different animal skeletons including two multi-animal set-ups. With an optional estimated imputation error, DISK enables behavior scien- tists to assess the reliability of the imputed data. The imputed recordings allow to detect more episodes of motion, such as steps, and to obtain more sta- tistically robust results when comparing these episodes between experimental conditions. While animal behavior experiments are expensive and complex, track- ing errors make sometimes large portions of the experimental data unusable. DISK allows for filling in the missing information and for taking full advantage of the rich behavioral data. This stand-alone imputation package, freely available at https://github.com/bozeklab/DISK.git, is applicable to results of any track- ing method (marker-based or markerless) and allows for any type of downstream analysis.

animal behavior and cognition↗

bioRxiv2023

Fast and efficient root phenotyping via pose estimation

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plants phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

plant biology↗

bioRxiv2023

Distinct developmental trajectories of autonomous behaviors and agency in rodents

Behavioral interactions within the nuclear family play a pivotal role in the emergence of agency: the capacity to regulate physiological, psychological and social needs. While behaviors may develop over days or weeks in line with nervous system maturation, individual behaviors can occur on sub-second time scales making it challenging to track development in lab studies with brief observation periods, or in field studies with limited temporal precision and animal identification. Here we study development in families of gerbils, a highly social rodent, collecting tens of millions of behavior time points and implementing machine learning methods to track individual subjects. We provided maturing gerbils with a large, undisturbed environment between postnatal day 15 and the age at which they would typically disperse from the family unit (day 30). We identified complex and distinct developmental trajectories for food and water acquisition, solitary exploration, and social behaviors, some of which displayed sex differences and diurnal patterns. Our work supports the emergence of well-delineated autonomous and social behavior phenotypes that correlate with specific periods and loci of neural maturation.

animal behavior and cognition↗

bioRxiv2022

Amygdalostriatal transition zone neurons encode sustained valence to direct conditioned behaviors

To ensure survival, the brain must rapidly identify threats and maintain defensive behaviors for as long as danger is present. The amygdala has been studied as a key site for fear responses, but responses to threat cues in amygdala neurons are largely transient and shorter in duration than defensive responses observed. Here, we present the amygdalostriatal transition zone (ASt) as a missing piece of the circuits mediating fear responses. Using single-nucleus RNA sequencing (snRNA-seq), we demonstrate that the ASt is genetically distinct from adjacent striatal and amygdalar structures. In vivo electrophysiology and calcium imaging reveal that ASt neurons have robust, sustained responses to shock-predicting cues. Further, photostimulation of the ASt is sufficient to drive freezing and avoidance behaviors, and optogenetic inhibition experiments show that Drd2+ ASt neurons are necessary for cue-conditioned fear responses. Our findings establish the ASt as a previously unappreciated yet critical structure for encoding learned associations and directing defensive behaviors.

neuroscience↗

bioRxiv2021

Isolation disrupts social interactions and destabilizes brain development in bumblebees

Social isolation, particularly in early life, leads to deleterious physiological and behavioral outcomes. Few studies, if any, have been able to capture the behavioral and neurogenomic consequences of early life social isolation together in a single social animal system. Here, we leverage new high-throughput tools to comprehensively investigate the impact of isolation in the bumblebee (Bombus impatiens) from behavioral, molecular, and neuroanatomical perspectives. We reared newly emerged bumblebees either in complete isolation, small groups, or in their natal colony, and then analyzed their behaviors while alone or paired with another bee. We find that when alone, individuals of each rearing condition show distinct behavioral signatures. When paired with a conspecific, bees reared in small groups or in the natal colony express similar behavioral profiles. Isolated bees, however, showed increased social interactions. To identify the neurobiological correlates of these differences, we quantified brain gene expression and measured the volumes of key brain regions for a subset of individuals from each rearing condition. Overall, we find that isolation increases social interactions and disrupts gene expression and brain development. Limited social experience in small groups is sufficient to preserve typical patterns of brain development and social behavior.

animal behavior and cognition↗

bioRxiv2020

SLEAP: Multi-animal pose tracking

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation to quantify and model natural animal behavior. This has led to important advances in deep learning-based markerless pose estimation that have been enabled in part by the success of deep learning for computer vision applications. Here we present SLEAP (Social LEAP Estimates Animal Poses), a framework for multi-animal pose tracking via deep learning. This system is capable of simultaneously tracking any number of animals during social interactions and across a variety of experimental conditions. SLEAP implements several complementary approaches for dealing with the problems inherent in moving from single-to multi-animal pose tracking, including configurable neural network architectures, inference techniques, and tracking algorithms, enabling easy specialization and tuning for particular experimental conditions or performance requirements. We report results on multiple datasets of socially interacting animals (flies, bees, and mice) and describe how dataset-specific properties can be leveraged to determine the best configuration of SLEAP models. Using a high accuracy model (<2.8 px error on 95% of points), we were able to track two animals from full size 1024 x 1024 pixel frames at up to 320 FPS. The SLEAP framework comes with a sophisticated graphical user interface, multi-platform support, Colab-based GPU-free training and inference, and complete tutorials available, in addition to the datasets, at sleap.ai.

animal behavior and cognition↗