Biology subjects

Jaksic, A. M.

Publications and source records attributed to Jaksic, A. M..

7 recordsLinked to original sources

bioRxiv2026

End-to-end automation of behavioural experiments with a collaborative robot

Reliable behavioural measurement is critical to neuroscience discovery, but end-to-end automation of experiments using live animals is challenging. Preparation and transfer of live animals into and out of behavioural assays require skilled, dexterous, gentle and consistent handling. This is difficult to achieve without bespoke automation systems, which resist wide adoption. We present STELA, a robotic cell featuring off-the-shelf collaborative robot (cobot) that autonomously runs the complete workflow of a Drosophila behaviour experiment, from vial to data. Designed to emulate human routine fly laboratory procedures in human workspace, it performs manipulation of fly culture vials, CO2 fly anaesthesia, vision-guided sex classification, single live fly manipulation, and loading and disassembly of a behaviour assay, all in a programmed loop. In 24-h working day, STELA completed 16 experiments (1024 individual visual learning assays in flies) compared to the human who, in an 8-hour working day, completed 6 (384 assays). Retention of flies fit for analysis was similar (93.85% versus 83.33%, respectively), but with 12-fold improvement in handling consistency by the robot. In biologically matched behaviour experiments, robotic and manual handling resulted in the same mean fly behavioural outcomes, yet the robot significantly reduced inter-individual variability in baseline and learning behaviours. STELA demonstrates that it is possible to deploy standard robotic systems to flexibly automate one of the most challenging animal experiments. It provides a low-variance, high-throughput platform for scalable experimentation, and a path to closed-loop, democratized robotic acceleration of experimental discovery.

animal behavior and cognition↗

bioRxiv2025

TopoTome: Topology-informed unsupervised segmentation and analysis of 3D images

Biological systems are three-dimensional and complex. Today, images of biological structures can be acquired using different imaging technologies and at increasing resolutions. However, identifying relevant structural features in three-dimensional (3D) images remains a significant challenge. 3D image segmentation is usually performed using deep-learning segmentation models. Such models are trained on manually annotated and segmented, dataset-specific images. Consequently, they rarely generalize across datasets. Here, we overcome these limitations with TopoTome, an unsupervised 3D image segmentation and analysis algorithm. TopoTome is based on topological data analysis, and conceptually distinct from standard clustering and deep learning image analysis models. It encodes the 3D image directly in topological space. Then, it performs unsupervised clustering to detect and segment features represented by salient and spatially coherent voxel intensity gradients. We demonstrate on simple, complex, synthetic and real-world 3D image data that it outperforms all 3D clustering algorithms. Its segmentation ranks with or outperforms best-in-class deep learning 3D image segmentation software. Beyond image segmentation, it provides streamlined topological data analysis of 3D images, advancing 3D image analysis from conventional mesh volumetry to structural topology. Owing to its conceptually different topological data analysis core, TopoTome does not need prior information and tuning to generalize across different imaging modalities, including fluorescence microscopy and X-ray computed tomography. We show it also readily generalizes across biological subjects, such as different species, organs and cells. TopoTome is thus one of the most versatile and accurate unsupervised 3D image segmentation algorithms.

bioinformatics↗

bioRxiv2024

Behavioral individuality is a consequence of experience, genetics and learning

Learning and memory are essential components of our individuality. While it is established that behaviour can vary across genetically identical individuals, it remains unknown how much of this variation stems from momentary experience during learning compared to genetics and its past interactions with the environment. To address this, we measured behaviour in thousands of flies from 90 genetic backgrounds while they performed tasks in conditions that either did or did not require learning. Flies that were genetically identical, raised under the same conditions and tested simultaneously in the same environment persistently modified the extent of expressed individuality when they could learn. This learning-induced residual expression of individuality and its dynamics were subdued or absent in innate, learning-independent behaviours. We could quantify and then recreate this phenomenon in computer simulations. The emergence of in silico behavioural individuality was most consistent with the individuality of real flies once we enabled reinforced learning in simulated agents. Moreover, we showed that minor differences in initial conditions of the experiment can exacerbate the expression of individuality within a genotype in a learning-dependent manner. Our results establish that besides the classical G x E interactions shared between individuals in the past, learning from individual momentary experience further extends the expression of individuality.

animal behavior and cognition↗

bioRxiv2021

Pre- and post-mating reproductive isolation evolve independently during rapid adaptation to high temperature

BackgroundEcological speciation and mutation-order speciation are two different mechanisms of adaptation-driven speciation. Both mechanisms predict different patterns of reproductive isolation for replicate populations adapting to the same environment. With ecological speciation, barriers to gene flow emerge between populations from different environments, but not among replicate populations from the same environment. Mutation-order speciation predicts reproductive isolation among populations adapted to the same environment. ResultsWe demonstrate that both speciation processes occurred within about 100 generations when replicate Drosophila simulans populations adapted to a novel, hot environment. Gene expression analysis identified the underlying molecular mechanisms. Premating ecological speciation is the byproduct of an altered lipid metabolism, which also changed the cuticular hydrocarbon (CHC) composition in hot-evolved flies. Postmating reproductive isolation supports mutation-order speciation most likely driven by co-evolution of reproduction-associated genes. ConclusionAdaptation processes can rapidly induce incipient speciation and different speciation mechanisms affect pre- and postmating reproductive isolation. We propose that the definition of mutation-order speciation should be expanded to account for polygenic processes from standing genetic variation.

evolutionary biology↗

bioRxiv2021

Evolution of phenotypic variance provides insights into the genetic basis of adaption

Most traits are polygenic and the contributing loci can be identified by GWAS. Their adaptive architecture is, however, difficult to characterize. Here, we propose to study the adaptive architecture of traits by monitoring the evolution of their phenotypic variance during adaptation to a new environment in well-defined laboratory conditions. Extensive computer simulations show that the evolution of phenotypic variance in a replicated experimental evolution setting can distinguish between oligogenic and polygenic adaptive architectures. We compared gene expression variance in male Drosophila simulans before and after 100 generations of adaptation to a novel hot environment. The variance change in gene expression was indistinguishable for genes with and without a significant change in mean expression after 100 generations of evolution. We suggest that a majority of adaptive gene expression evolution can be explained by a polygenic architecture. We propose that tracking the evolution of phenotypic variance across generations can provide an approach to characterize the adaptive architecture. Significant StatementIt is widely accepted that most complex traits have a polygenic basis. Nevertheless, it is difficult to predict which of these loci are responding to selection when a population is exposed to a new selection regime. To address this situation, we propose to infer the adaptive architecture for traits by tracking the evolution of their phenotypic variance during adaptation to a new environment. As a case study, we analyze the evolution of gene expression variance in outbred Drosophila simulans populations adapting to a new temperature regime to infer the genetic architecture of adaptive gene expression evolution. We suggested that the adaptive gene expression evolution is better explained by a polygenic architecture.

evolutionary biology↗

bioRxiv2019

Neuronal function and dopamine signaling evolve at high temperature in Drosophila

Introduction Introduction Main Methods Author contributions Competing interest Materials and Correspondence References Adaptation to local environments is a general principle in nature. In the light of the ongoing climatic change, the mechanisms for temperature adaptation are of particular interest1-2. Many essential biological processes are temperature sensitive, and neuronal activity3-7 is a particularly prominent one as it affects essential, fitness-related traits such as behavior and locomotion8. Here, we investigate if temperature stress triggers adaptation of neuronal signaling. We used replicated laboratory evolution experiments with Drosophila simula ...

evolutionary biology↗

bioRxiv2018

Polygenic adaptation fuels genetic redundancy in Drosophila

The genetic architecture of adaptive traits is of key importance to predict evolutionary responses. Most adaptive traits are polygenic - i.e. result from selection on a large number of genetic loci - but most molecularly characterized traits have a simple genetic basis. This discrepancy is best explained by the difficulty in detecting small allele frequency changes across many contributing loci. To resolve this, we use laboratory natural selection, a framework that is powerful enough to detect signatures for selective sweeps and polygenic adaptation. We exposed 10 replicates of a Drosophila simulans population to a new temperature regime and uncovered a polygenic architecture of an adaptive trait with high genetic redundancy among adaptive alleles. We observed convergent phenotypic responses, e.g. fitness, metabolic rate and fat content, and a strong polygenic response (99 selected alleles; mean s=0.061). However, each of these selected alleles increased in frequency only in a subset of the evolving replicates. Our results show that natural D. simulans populations harbor a vast reservoir of adaptive variation facilitating rapid evolutionary responses. The observed genetic redundancy potentiates this genotypic variation through multiple genetic pathways leading to phenotypic convergence. This key property of adaptive alleles requires the modification of testing strategies in natural populations beyond the search for convergence on the molecular level.

evolutionary biology↗