Biology subjects

Ranefall, P.

Publications and source records attributed to Ranefall, P..

4 recordsLinked to original sources

bioRxiv2023

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy

Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to discriminate four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use. Author SummaryBacterial infections are a leading cause of premature death worldwide, and growing antibiotic resistance is making treatment increasingly challenging. To effectively treat a patient with a bacterial infection, it is essential to quickly detect and identify the bacterial species and determine its susceptibility to different antibiotics. Prompt and effective treatment is crucial for the patients survival. A microfluidic device functions as a miniature "lab-on-chip" for manipulating and analyzing tiny amounts of fluids, such as blood or urine samples from patients. Microfluidic chips with chambers and channels have been designed for quickly testing bacterial susceptibility to different antibiotics by analyzing bacterial growth. Identifying bacterial species has previously relied on killing the bacteria and applying species-specific fluorescent probes. We introduce deep learning models as a fast and cost-effective method for identifying bacteria species directly from phase-contrast microscopy images of living bacteria simultaneously as growth is analyzed. We envision this method being employed concurrently with antibiotic susceptibility tests in future applications, significantly enhancing bacterial infection treatments.

bioinformatics↗

bioRxiv2019

SynQuant: An Automatic Tool to Quantify Synapses from Microscopy Images

MotivationSynapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synapse imaging data, several issues prevent satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness for different neurites and synapses is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio (SNR) due to constraints of experiments and availability of sensitive antibodies. The combination of these issues makes the detection of synapses challenging and necessitates developing a new tool to accurately and reliably quantify synapses. ResultsWe present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. Through extensive experiments on both synthetic and real images in the presence of severe antibody diffusion, high heterogeneity, and large noise, our method was demonstrated to outperform peer specialized synapse detection tools as well as generic spot detection methods by a large margin. Finally, we show SynQuant reliably uncovers statistically significant differences between disease and control conditions in a neuron-astrocyte co-culture based model of Down Syndrome. AvailabilityThe Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. Contactyug@vt.edu

neuroscience↗

bioRxiv2018

Zebrafish larvae as a model system for systematic characterization of drugs and genes in dyslipidemia and atherosclerosis

BackgroundHundreds of loci have been robustly associated with circulating lipids, atherosclerosis and coronary artery disease; but for most loci the causal genes and mechanisms remain uncharacterized.\n\nMethodsWe developed a semi-automated experimental pipeline for systematic, quantitative, large-scale characterization of mechanisms, drugs and genes associated with dyslipidemia and atherosclerosis in a zebrafish model system. We validated our pipeline using a dietary (n>2000), drug treatment (n>1000), and genetic intervention (n=384), and used it to characterize three candidate genes in a GWAS-identified pleiotropic locus on chr 19p13.11 (n>500).\n\nResultsOur results show that five days of overfeeding and cholesterol supplementation had independent pro-atherogenic effects, which could be diminished by concomitant treatment with atorvastatin and ezetimibe. CRISPR-Cas9-induced mutations in orthologues of proof-of-concept genes resulted in higher LDL cholesterol levels (apoea), and more early stage atherosclerosis (apobb.1). Finally, our pipeline helped identify putative causal genes for circulating lipids and early-stage atherosclerosis (LPAR2 and GATAD2A).\n\nConclusionsIn summary, our pipeline facilitates systematic, in vivo characterization of drugs and candidate genes to increase our understanding of disease etiology, and can likely help identify novel targets for therapeutic intervention.

genomics↗

bioRxiv2018

In situ quantification of individual mRNA transcripts in melanocytes discloses gene regulation of relevance to speciation

Functional validation of candidate genes for adaptation and speciation remains challenging. We here exemplify the utility of a method quantifying individual mRNA transcripts in revealing the molecular basis of divergence in feather pigment synthesis during early-stage speciation in crows. Using a padlock probe assay combined with rolling circle amplification we quantified cell-type specific gene expression in the native, histological context of growing feather follicles. Expression of Tyrosinase related protein 1 (TYRP1), Solute carrier family 45 member 2 (SLC45A2) and Hematopoietic prostaglandin D synthase (HPGDS) was melanocyte-limited and significantly reduced in follicles from hooded crow explaining the substantially lower melanin content in grey vs. black feathers. The central upstream transcription factor Microphthalmia-associated transcription factor (MITF) only showed differential expression specific to melanocytes - a feature not captured by bulk RNA-seq. Overall, this study provides insight into the molecular basis of an evolutionary young transition in pigment synthesis, and demonstrates the power of histologically explicit, statistically substantiated single-cell gene expression quantification for functional genetic inference in natural populations.

evolutionary biology↗