Biology subjects

Traniello, I. M.

Publications and source records attributed to Traniello, I. M..

10 recordsLinked to original sources

bioRxiv2025

RESCUE: recovery of idiosyncratic expression patterns in spatial transcriptomics

Spatial transcriptomics (ST) enables gene expression profiling while preserving the spatial architecture of intact tissue. Analyzing ST data often proceeds by first extracting cell-level information, typically through cell segmentation or cell-type deconvolution. However, a critical oversight has been that a substantial portion of molecular expression is systematically lost or unannotated by these methods. This lost expression can arise from diverse and biologically important sources like fragile or underrepresented cell types, subcellular structures like neurites, and extracellular expression. These omissions can result in biased analyses and incorrect or incomplete biological interpretations. We describe a new computational method, RESCUE, that can recover the unattributed spatial expression patterns missed by existing ST analysis methods and enable robust inference even when reference is incomplete. We validate RESCUE using MERFISH data from the honey bee brain and apply it to multiple ST datasets to demonstrate how it can reveal novel insights into complex tissue biology.

bioinformatics↗

bioRxiv2025

Queen loss unmasks cryptic worker influence and decentralizes the bumble bee social network

Dominant individuals often structure group organization, but less is known about how social networks reorganize in their absence and how variation among subordinates contributes to collective outcomes. Bumble bees (Bombus impatiens) provide an ideal system to study these dynamics: queens typically monopolize reproduction, but in some contexts individual workers can adopt queenlike social roles. Using multi-animal pose tracking, we compared matched queenright and queenless partitions from the same source colonies, quantifying over 80 million social interactions. Queen-less colonies exhibited increased behavioral variation and contained a subset of highly influential workers with elevated movement, spatial centrality, and reproductive activity that was absent in queen-right conditions. The emergence of these individuals coincided with a shift from centralized to decentralized, efficient network architectures. These results demonstrate that queen presence constrains latent worker variation, revealing how individual behavioral differences can scale up to reshape collective social organization in hierarchical societies.

animal behavior and cognition↗

bioRxiv2024

pipemake: A pipeline creation tool using Snakemake for reproducible analysis of biological datasets

The exponential growth in biological data generation has created an urgent need for efficient, reproducible computational analysis workflows. Here, we present pipemake, a computational platform designed to streamline the development and implementation of efficient and reproducible Snakemake workflows. pipemake creates modular pipelines that can be seamlessly integrated or removed from the platform without requiring reconfiguration of the core system, enabling flexible adaptation of workflows to different analytical needs across diverse fields. To demonstrate the platforms capabilities, we created and implemented pipelines to reanalyze two distinct biological datasets. First, we recreated a population genomics analysis of the socially flexible halictid bee, Lasioglossum albipes, using pipemake-generated workflows for de novo genome annotation, processing of variant data, dimensionality reduction, and a genome-wide association study (GWAS). We then used pipemake to analyze behavioral tracking data from the common eastern bumble bee, Bombus impatiens. In both cases, pipemake workflows produced results consistent with published findings while substantially reducing hands-on analysis time. Overall, pipemakes modular design allows researchers to easily modify existing pipelines or develop new ones without software development expertise. Beyond streamlining workflow creation, pipemake leverages the full Snakemake ecosystem to enable parallel processing, automated error recovery, and comprehensive analysis documentation. These features make pipemake an efficient and accessible solution for analyzing complex biological datasets. pipemake is freely available as a conda package or direct download at https://github.com/kocherlab/pipemake

bioinformatics↗

bioRxiv2024

Molecular analyses of individual variation in honey bee sociability

Individual variation in sociability is a central feature of every society. This includes honey bees, with some individuals well-connected and sociable, and others at the periphery of their colonys social network. However, the genetic and molecular bases of sociability are poorly understood. Trophallaxis - a behavior involving sharing liquid with nutritional and signaling properties - comprises a social interaction and a proxy for sociability in honey bee colonies: more sociable bees engage in more trophallaxis. Here we identify genetic and molecular mechanisms of trophallaxis-based sociability by combining genome sequencing, brain transcriptomics, and automated behavioral tracking. A genome-wide association study (GWAS) identified 18 single-nucleotide polymorphisms (SNPs) associated with variation in sociability. Several SNPs were localized to genes previously associated with sociability in other species, including in the context of human autism, suggesting shared molecular mechanisms of sociability. Variation in sociability also was linked to differential brain gene expression, particularly genes associated with neural signaling and development. Using comparative genomic and transcriptomic approaches, we also detected evidence for divergent mechanisms underpinning sociability across species, including those related to reward sensitivity and encounter probability. These results highlight both potential evolutionary conservation of the molecular roots of sociability and points of divergence.

genomics↗

bioRxiv2024

SpaceExpress: a method for comparative spatial transcriptomics based on intrinsic coordinate systems of tissues

Spatial transcriptomics (ST) technologies have enabled new explorations into the spatial organization of tissues and their functional implications. However, one of the most fundamental analyses - comparative analysis of spatial gene expression across phenotypes - remains a formidable challenge. We introduce SpaceExpress, a novel statistical tool for detecting phenotype-associated changes in spatial expression patterns. SpaceExpress employs a neural network to embed multiple ST samples in a common latent space, enabling robust cross-sample comparisons despite structural and technical variations. It then uses spline regression to test differential spatial expression of genes between conditions, identifying specific regions of the tissue where expression patterns diverge and quantifying the magnitude of those differences, with rigorous false discovery control and handling of multiple replicates per condition. It includes visualization tools to help interpret spatial pattern differences. We demonstrate the tools effectiveness on synthetic and real ST datasets, revealing mechanistic insights into behavior- and development-related neurogenomic changes in honey bees and mice. Our work extends the highly influential paradigm of differential gene expression analysis to spatial omics.

bioinformatics↗

bioRxiv2023

Developmental transcriptomes predict adult social behaviors in the socially flexible sweat bee, Lasioglossum baleicum

Natural variation can provide important insights into the genetic and environmental factors that shape social behavior and its evolution. The sweat bee, Lasioglossum baleicum, is a socially flexible bee capable of producing both solitary and eusocial nests. We demonstrate that within a single nesting aggregation, soil temperatures are a strong predictor of the social structure of nests. Sites with warmer temperatures in the spring have a higher frequency of social nests than cooler sites, perhaps because warmer temperatures provide a longer reproductive window for those nests. To identify the molecular correlates of this behavioral variation, we generated a de novo genome assembly for L. baleicum, and we used transcriptomic profiling to compare adults and developing offspring from eusocial and solitary nests. We find that adult, reproductive females have similar expression profiles regardless of social structure in the nest, but that there are strong differences between reproductive females and workers from social nests. We also find substantial differences in the transcriptomic profiles of stage-matched pupae from warmer, social-biased sites compared to cooler, solitary-biased sites. These transcriptional differences are strongly predictive of adult reproductive state, suggesting that the developmental environment may set the stage for adult behaviors in L. baleicum. Together, our results help to characterize the molecular mechanisms shaping variation in social behavior and highlight a potential role of environmental tuning during development as a factor shaping adult behavior and physiology in this socially flexible bee.

genomics↗

bioRxiv2022

NAPS: Integrating pose estimation and tag-based tracking

O_LISignificant advances in computational ethology have allowed the quantification of behavior in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both. C_LIO_LITo capture finely resolved behaviors while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state-of-the-art, deep learning-based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens). C_LIO_LIWe provide a stand-alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioral variation within individuals in a group. C_LIO_LIExpanding the toolkit for capturing the constituent behaviors of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviors and can provide critical data for understanding how individual variation influences collective dynamics. C_LI

animal behavior and cognition↗

bioRxiv2022

Evolution of decreased aggression in honey bees targeted brain cell gene regulatory networks

Understanding how genotypic variation results in phenotypic variation, a major challenge in biology, is especially difficult for collective behaviour because collective group phenotypes arise from complex interactions between group members1. Honeybees aggressively defend their colony from attacks with highly integrated collective behaviour in which different groups of bees play specific roles, giving rise to distinct colony-level differences in aggression. A previous genome-wide association study of a population of Africanized honeybees (Apis mellifera scutellata) from Puerto Rico that recently evolved decreased aggression identified hundreds of genes with single nucleotide polymorphisms (SNPs) that associated with colony-level variation in aggression2. Many of these SNPs also showed strong signals of selection for decreased aggression2,3, but their influence on brain function was unknown. Using brain single-cell (sc) transcriptomics and sc gene regulatory network analysis, we show here that variants of these genes give rise to genetic differences in transcription factor-target gene relationships. These differences involved the activity of several TFs, some that have been previously associated with aggression, like single stranded-binding protein c31A, and some that have been associated with tissue morphogenesis but not behaviour, like apontic. The activity of these and other TFs was located in specific brain cell populations related to olfaction and vision, the two sensory modalities that bees use in colony defence. They also implicate metabolism of serotonin, a neurochemical already known to influence honeybee aggression, but not from a genetic perspective. Surprisingly, genetic differences were more pronounced in the brains of forager bees than in similarly aged but more aggressive soldier bees, pointing to an evolutionary change in division of labour for colony defence. Our results demonstrate how group genetics can shape a collective phenotype by modulating individual brain gene regulatory network architecture.

genomics↗

bioRxiv2020

SimiC: A Single Cell Gene Regulatory Network Inference method with Similarity Constraints

Single-cell RNA-Sequencing has made it possible to infer high-resolution gene regulatory networks (GRNs), providing deep biological insights by revealing regulatory interactions at single-cell resolution. However, current single-cell GRN analysis methods produce only a single GRN per input dataset, potentially missing relationships between cells from different phenotypes. To address this issue, we present SimiC, a single-cell GRN inference method that produces a GRN per phenotype while imposing a similarity constraint that forces a smooth transition between GRNs, allowing for a direct comparison between different states, treatments, or conditions. We show that jointly inferring GRNs can uncover variation in regulatory relationships across phenotypes that would have otherwise been missed. Moreover, SimiC can recapitulate complex regulatory dynamics across a range of systems, both model and non-model alike. Taken together, we establish a new approach to quantitating regulatory architectures between the GRNs of distinct cellular phenotypes, with far-reaching implications for systems biology.

bioinformatics↗

bioRxiv2020

Parallel mechanisms of visual memory formation across distinct regions of the honey bee brain

Visual learning is vital to the behavioral ecology of the Western honey bee (Apis mellifera). Honey bee workers forage for floral resources, a behavior that requires the learning and long-term memory of visual landmarks, but how these memories are mapped to the brain remains poorly understood. To address this gap in our understanding, we collected bees that successfully learned visual associations in a conditioned aversion paradigm and compared gene expression correlates of memory formation in the mushroom bodies, a higher-order sensory integration center classically thought to contribute to learning, as well as the optic lobes, the primary visual neuropil responsible for sensory transduction of visual information. We quantitated expression of CREB and CaMKii, two classical genetic markers of learning and fen-1, a gene specifically associated with punishment learning in vertebrates. As expected, we report substantial involvement of the mushroom bodies for all three markers but additionally demonstrate an unexpected involvement of the optic lobes across a similar time course. Our findings imply the molecular involvement of a sensory neuropil during visual associative learning parallel to a higher-order brain region, furthering our understanding of how a tiny brain processes environmental signals.

animal behavior and cognition↗