Biology subjects

Golas, S.

Publications and source records attributed to Golas, S..

4 recordsLinked to original sources

bioRxiv2023

Low-cost camera-based estrous tracking enables transgenesis in Peromyscus leucopus, the primary reservoir for Lyme disease

CRISPR/Cas9 technology has revolutionized the production of animal models by reducing experimental timelines, slashing costs and streamlining gene editing, leading to a rapid expansion in the number of unique models for the study of human disease and gene function. However, most non-model animals, many of which are important in cancer and aging research, remain recalcitrant to genome engineering due to our limited understanding of their reproductive biology. Many wild rodents that transmit human diseases remain particularly challenging to engineer due to low pregnancy rates, the lack of external copulatory plugs, and susceptibility to premature termination of pregnancy. Here, we present low-cost activity-based estrous tracking for the efficient generation of timed pregnant and pseudopregnant white-footed mice and extend this tracking method to both lab mice and hamsters. Leveraging this technology, we demonstrate the generation of engineered Peromyscus leucopus, the primary reservoir for Lyme disease-causing bacteria and a putative model organism for studies of aging. These tools have broad implications for biomedical research and ecological engineering. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/563285v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@19a3fb2org.highwire.dtl.DTLVardef@1cbce4corg.highwire.dtl.DTLVardef@1551e0corg.highwire.dtl.DTLVardef@127c028_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

PyLabRobot: An Open-Source, Hardware Agnostic Interface for Liquid-Handling Robots and Accessories

Liquid handling robots are often limited by proprietary programming interfaces that are only compatible with a single type of robot and operating system, restricting method sharing and slowing development. Here we present PyLabRobot, an open-source, cross-platform Python interface capable of programming diverse liquid-handling robots, including Hamilton STARs, Tecan EVOs, and Opentron OT-2s. PyLabRobot provides a universal set of commands and representations for deck layout and labware, enabling the control of diverse accessory devices. The interface is extensible and can work with any robot that manipulates liquids within a Cartesian coordinate system. We validated the system through unit tests and several application demonstrations, including a browser-based simulator, a position calibration tool, and a path-teaching tool for complex movements. PyLabRobot provides a flexible, open, and collaborative programming environment for laboratory automation. Figure Abstract O_FIG O_LINKSMALLFIG WIDTH=124 HEIGHT=200 SRC="FIGDIR/small/547733v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@9dbd4forg.highwire.dtl.DTLVardef@1f7cc6dorg.highwire.dtl.DTLVardef@18e0d37org.highwire.dtl.DTLVardef@105cd0b_HPS_FORMAT_FIGEXP M_FIG C_FIG PyLabRobot overcomes the limitations of proprietary robotic systems. (a) Scientists with access to liquid-handling robots are currently limited by proprietary interfaces that require specialized knowledge, hinder cross-platform operability, and restrict sharing of methods among different robot types. For complex tasks, many researchers need assistance from a specialist familiar with their particular system, most notably when creating or editing protocols. (b) PyLabRobot (https://github.com/PyLabRobot/pylabrobot) offers a single interface that allows any person with basic Python skills to program diverse types of liquid-handling robots and share protocols freely, fostering a more collaborative and efficient research environment. The Python API makes it easy to interact with a large scientific computing ecosystem and allows users to leverage large language models for programming assistance.

bioengineering↗

bioRxiv2022

High-throughput Approaches to Uncover Synergistic Drug Combinations in Leukemia

We report a comprehensive drug synergy study in acute myeloid leukemia (AML). In this work, we investigate 11 cell lines spanning both MLL-rearranged and non-rearranged subtypes. The work comprises a resource for the community, with many synergistic drug combinations that could not have been predicted a priori, and open source code for automation and analyses. We base our definitions of drug synergy on the Chou-Talalay method, which is useful for visualizations of synergy experiments in isobolograms, and median-effects plots, among other representations. Our key findings include drug synergies affecting the chromatin state, specifically in the context of regulation of the modification state of histone H3 lysine-27. We report open source high throughput methodology such that multidimensional drug screening can be accomplished with equipment that is accessible to most laboratories. This study will enable preclinical investigation of new drug combinations in a lethal blood cancer, with data analysis and automation workflows freely available to the community. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/518409v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1e85ef9org.highwire.dtl.DTLVardef@b3369eorg.highwire.dtl.DTLVardef@1c6d705org.highwire.dtl.DTLVardef@14ec6b2_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2019

CCSeq: Clusters of Colocalized Sequences

0.1 MotivationPotential transcription factor (TF) complexes may be identified by testing whether the binding sequences of individual TF proteins form clusters with each other. These clusters may also indicate TF inhibition due to competitive occupancy of enhancer regions. Genome annotation data containing the coordinates of enhancer sequences is highly accessible via position-weight matrix tools.\n\n0.2 ResultsAn algorithm called CCSeq (Clusters of Colocalized Sequences) was developed for identifying clusters of sequences along a one-dimensional line, such as a chromosome, given genome annotation files and a cut-off distance as inputs. The algorithm was applied to the binding sequences of the constituent proteins of two known transcription factor complexes, the HSF1 homotrimer and one form of the NF-{kappa}B complex, a dimer of NFKB2 and RELB. 28 clusters of HSF1 trimer binding sequences were identified on chromosome Y, and 16 clusters of the NFKB2 and RELB dimer were identified on chromosome 17, compared to 0 clusters identified in any of the five simulated random distributions for each of the two sets of TF proteins. Additionally, structural patterns of these binding sequence clusters are described.\n\n0.3 Availability and ImplementationThis algorithm is freely available as an R package on the open source R repository CRAN at the following link: https://cran.r-project.org/package=colocalized. Genome annotation files were obtained from the PWMScan tool at https://ccg.epfl.ch/pwmtools/pwmscan.php hosted by the Swiss Insitute of Bioinformatics (2) (3).

bioinformatics↗