Biology subjects

Karempudi, P.

Publications and source records attributed to Karempudi, P..

4 recordsLinked to original sources

bioRxiv2024

One-day phenotypic drug susceptibility testing for Mycobacterium tuberculosis variant bovis BCG using single-cell imaging and a deep neural network

Drug-resistant tuberculosis (TB) kills approximately 200,000 people every year. A contributing factor is the slow turnaround time associated with anti-tuberculosis drug susceptibility diagnostics. The prevailing gold standard for phenotypic drug susceptibility testing (pDST) takes at least two weeks. In this study, we used Mycobacterium tuberculosis variant bovis BCG (M. bovis BCG) and Mycobacterium smegmatis as models for tuberculous and nontuberculous pathogens. The bacteria were loaded into a microfluidic chip, trapping them in microchambers, and allowing simultaneous tracking of single-cell growth with and without antibiotic exposure. A deep neural network image-segmentation algorithm was employed to quantify the growth rate over time and determine how the strains responded to the drugs compared to the untreated reference. We determined that the response time of the susceptible strains to isoniazid (INH), ethambutol (EMB), and linezolid (LZD) at MIC was within 3 hours and 1.5 hours for M. bovis BCG and M. smegmatis, respectively. Resistant strains of M. smegmatis were identifiable within 3 hours, suggesting that growth-based pDST can be conducted in less than 12 hours for slow-growing M. bovis BCG. The results obtained for M. bovis BCG are most likely comparable to what we expect for M. tuberculosis as these strains share 99.96% genetic identity.

microbiology↗

bioRxiv2023

Three-dimensional localization of fluorescent proteins in living Escherichia coli

3D localization of fluorescent proteins (FPs) in living bacteria has been challenging due to the low signal-to-background ratio of the FPs and the relatively uncertain positioning of the cells in the optical reference system. Using mother-machine microfluidic devices together with deep learning, we present an approach that enables accurate 3D localization of FPs in Escherichia coli over long periods. We describe a method to simulate ground truth training data for the deep learning network based on background models generated from experimental data. We test the method by studying how chromosomal loci are relocated in 3D over the E. coli cell cycle. Since the cells are radially symmetric, we expect the same width and height distribution of fluorophores if the 3D positions are correctly determined. We observe this pattern experimentally for all the labelled loci on the chromosome. Interestingly, some loci are located exclusively in the periphery of the nucleoid, while others are more confined to the core of the nucleoid. This method enables studying any chromosomal loci inside living E. coli cells in high-throughput.

biophysics↗

bioRxiv2023

Real-time pooled optical screening with single-cell isolation capability.

In a pooled optical screen, a genetically diverse library of living cells is imaged and characterised for phenotypic variations without knowing the genotype of the cells. The genotypes are identified in situ after the cells have been fixed or by physical extraction of interesting phenotypes followed by sequencing. Mother-machine microfluidics devices are efficient tools in pooled optical screens since many strains can be imaged in the same field of view, but the throughput is often limited. In this work, we show a method to extract single bacterial cells from a compact 100,000-trap mother-machine-based fluidic device using an optical tweezer. Unlike previous devices, the fluids in our design are routed in 3D to enable fast loading of cells, increased trap density, and faster imaging. We have also developed software that allows real-time analysis of the phenotyping data.

biophysics↗

bioRxiv2021

Rapid antibiotic susceptibility testing and species identification for mixed infections

Antimicrobial resistance is an increasing problem globally. Rapid antibiotic susceptibility testing (AST) is urgently needed in the clinic to enable personalized prescription in high-resistance environments and limit the use of broad-spectrum drugs. Previously we have described a 30 min AST method based on imaging of individual bacterial cells. However, current phenotypic AST methods do not include species identification (ID), leaving time-consuming plating or culturing as the only available option when ID is needed to make the sensitivity call. Here we describe a method to perform phenotypic AST at the single-cell level in a microfluidic chip that allows subsequent genotyping by in situ FISH. By stratifying the phenotypic AST response on the species of individual cells, it is possible to determine the susceptibility profile for each species in a mixed infection sample in 1.5 h. In this proof-of-principle study, we demonstrate the operation with four antibiotics and a mixed sample with four species.

microbiology↗