Single Cell Analysis of Drug Distribution by Intravital Imaging

Recent advances in the field of intravital imaging have for the first time allowed us to conduct pharmacokinetic and pharmacodynamic studies at the single cell level in live animal models. Due to these advances, there is now a critical need for automated analysis of pharmacokinetic data. To address this, we began by surveying common thresholding methods to determine which would be most appropriate for identifying fluorescently labeled drugs in intravital imaging. We then developed a segmentation algorithm that allows semi-automated analysis of pharmacokinetic data at the single cell level. Ultimately, we were able to show that drug concentrations can indeed be extracted from serial intravital imaging in an automated fashion. We believe that the application of this algorithm will be of value to the analysis of intravital microscopy imaging particularly when imaging drug action at the single cell level

MitoSegNet: Easy-to-use Deep Learning Segmentation for Analyzing Mitochondrial Morphology

While the analysis of mitochondrial morphology has emerged as a key tool in the study of mitochondrial function, efficient quantification of mitochondrial microscopy images presents a challenging task and bottleneck for statistically robust conclusions. Here, we present Mitochondrial Segmentation Network (MitoSegNet), a pretrained deep learning segmentation model that enables researchers to easily exploit the power of deep learning for the quantification of mitochondrial morphology. We tested the performance of MitoSegNet against three feature-based segmentation algorithms and the machine-learning segmentation tool Ilastik. MitoSegNet outperformed all other methods in both pixelwise and morphological segmentation accuracy. We successfully applied MitoSegNet to unseen fluorescence microscopy images of mitoGFP expressing mitochondria in wild-type and catp-6ATP13A2 mutant C. elegans adults. Additionally, MitoSegNet was capable of accurately segmenting mitochondria in HeLa cells treated with fragmentation inducing reagents. We provide MitoSegNet in a toolbox for Windows and Linux operating systems that combines segmentation with morphological analysis

IMC-Denoise: A content aware denoising pipeline to enhance Imaging Mass Cytometry

Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging technology for analyzing complex microenvironments using more than 40 molecularly-specific channels. However, this modality has unique data processing requirements, particularly for patient tissue specimens where signal-to-noise ratios for markers can be low, despite optimization, and pixel intensity artifacts can deteriorate image quality and downstream analysis. Here we demonstrate an automated content-aware pipeline, IMC-Denoise, to restore IMC images deploying a differential intensity map-based restoration (DIMR) algorithm for removing hot pixels and a self-supervised deep learning algorithm for shot noise image filtering (DeepSNiF). IMC-Denoise outperforms existing methods for adaptive hot pixel and background noise removal, with significant image quality improvement in modeled data and datasets from multiple pathologies. This includes in technically challenging human bone marrow; we achieve noise level reduction of 87% for a 5.6-fold higher contrast-to-noise ratio, and more accurate background noise removal with approximately 2 × improved F1 score. Our approach enhances manual gating and automated phenotyping with cell-scale downstream analyses. Verified by manual annotations, spatial and density analysis for targeted cell groups reveal subtle but significant differences of cell populations in diseased bone marrow. We anticipate that IMC-Denoise will provide similar benefits across mass cytometric applications to more deeply characterize complex tissue microenvironments

Structure-dependent amplification for denoising and background correction in Fourier ptychographic microscopy

Fourier Ptychographic Microscopy (FPM) allows high resolution imaging using iterative phase retrieval to recover an estimate of the complex object from a series of images captured under oblique illumination. FPM is particularly sensitive to noise and uncorrected background signals as it relies on combining information from brightfield and noisy darkfield (DF) images. In this article we consider the impact of different noise sources in FPM and show that inadequate removal of the DF background signal and associated noise are the predominant cause of artefacts in reconstructed images. We propose a simple solution to FPM background correction and denoising that outperforms existing methods in terms of image quality, speed and simplicity, whilst maintaining high spatial resolution and sharpness of the reconstructed image. Our method takes advantage of the data redundancy in real space within the acquired dataset to boost the signal-to-background ratio in the captured DF images, before optimally suppressing background signal. By incorporating differentially denoised images within the classic FPM iterative phase retrieval algorithm, we show that it is possible to achieve efficient removal of background artefacts without suppression of high frequency information. The method is tested using simulated data and experimental images of thin blood films, bone marrow and liver tissue sections. Our approach is non-parametric, requires no prior knowledge of the noise distribution and can be directly applied to other hardware platforms and reconstruction algorithms making it widely applicable in FPM

Automated methods for tuberculosis detection/diagnosis : a literature review

Funding: Welcome Trust Institutional Strategic Support fund of the University of St Andrews, grant code 204821/Z/16/Z.Tuberculosis (TB) is one of the leading infectious causes of death worldwide. The effective management and public health control of this disease depends on early detection and careful treatment monitoring. For many years, the microscopy-based analysis of sputum smears has been the most common method to detect and quantify Mycobacterium tuberculosis (Mtb) bacteria. Nonetheless, this form of analysis is a challenging procedure since sputum examination can only be reliably performed by trained personnel with rigorous quality control systems in place. Additionally, it is affected by subjective judgement. Furthermore, although fluorescence-based sample staining methods have made the procedure easier in recent years, the microscopic examination of sputum is a time-consuming operation. Over the past two decades, attempts have been made to automate this practice. Most approaches have focused on establishing an automated method of diagnosis, while others have centred on measuring the bacterial load or detecting and localising Mtb cells for further research on the phenotypic characteristics of their morphology. The literature has incorporated machine learning (ML) and computer vision approaches as part of the methodology to achieve these goals. In this review, we first gathered publicly available TB sputum smear microscopy image sets and analysed the disparities in these datasets. Thereafter, we analysed the most common evaluation metrics used to assess the efficacy of each method in its particular field. Finally, we generated comprehensive summaries of prior work on ML and deep learning (DL) methods for automated TB detection, including a review of their limitations.Publisher PDFPeer reviewe

Methods for functional characterization of transcription factor binding sites in bacteria

Thesis (Ph.D.)--Boston UniversityUnderstanding gene regulation is necessary to gain insight into and model important cellular processes including disease. Current inability to combat many diseases is partly because of incomplete understanding of gene circuitry. Regulation mechanisms of Mycobacterium tuberculosis, the causative agent of Tuberculosis are not properly understood. Transcriptional regulatory network (TRN) is a network comprising transcription factors (TF) and their targeted genes that provide a powerful framework to analyze the complete regulatory system. Chromatin immunoprecipitation followed by next generation sequencing (ChiP-Seq) is becoming the method of choice to identify genome wide TFBS . Therefore, we use ChiP-Seq on known transcription factors to reconstruct the TRN of Mycobacterium tuberculosis (Mtb) and other bacteria. ChiP-Seq reveals various transcription factor binding sites (TFBS) but doesn't provide any information on the mechanism of regulation of the genes by their corresponding TF's. Techniques to gain more insight into the mechanisms include microarray, knock out studies and qPCR. But, these techniques provide a static view of network. Also, they provide information at RNA level and mask the regulation happening at protein level. Therefore, in order to understand both the mechanism of regulation at protein level as well as to capture the network dynamics, we built a synthetic gene circuit in Mycobacterium smegmatis and defined input-output relationships between key TFs and their targeted promoters. We validated this system on kstR, a TF which is a known repressor. KstR regulates genes involved in cholesterol degradation and is shown to de- repress itself and its regulon genes in the presence of cholesterol as well as in hypoxia, where there are no exogenous lipids4- . We explored the possibility of other by-products that may be responsible for the de-repression of kstR and its regulon. The data suggests that propionyl-coA, a by-product from degradation of cholesterol, odd numbered fatty acids as well as branched chain amino-acids is causing the de-repression of kstR and its regulon. ChiP-Seq data on transcription factors in MTb as well as E.coli shows that many TFBS are located immediately upstream of open reading frame start sites, consistent with our understanding ofprokaryotic gene regulation. However, the data also suggests that many TFBS are located inside and also downstream of open reading frames6. One of our hypotheses is that these novel TFBS might be indirect binding sites that mediate chromatin looping . Therefore, we developed a method 3C (Chromosome Conformation Capture) to understand the regulation in the third dimension by analyzing the chromosomal interactions. We optimized the protocol in E.coli and validated using a known interaction mediated by a repressor GalR . We then identified two regions, 20 kbp apart, containing TFBS of StpA, a nucleoid associated protein, which are not directly involved in gene regulation of their downstream genes. The data from a 3C experiment on an E.coli strain with inducible StpA suggests that these two regions interact by an unknown mechanism. However, the interaction was not lost when a similar experiment is done in StpA knock out strain suggesting that StpA may not be a sole TF responsible for this interaction. Lastly, we developed Hi-C method on E.coli genomic DNA to identify long range interactions in a genome wide and unbiased manner

Micropattern traction microscopy: a technique for the simplification of cellular traction force measurements

Thesis (Ph.D.)--Boston UniversityCells respond to a number of cues that affect how they interact with their surrounding environment, such as topology, the presentation of adhesive ligands, and stiffness. Recent advancements in the field ofmechanobiology have revealed that one of the main ways in which cells sense these cues is through contractile forces. Mechanobiology research seeks to understand how environmental cues affect the forces that cells exert on their surronnding environment and how these mechanical forces are communicated to the cell and transformed into biochemical signals. Therefore, quantitative methods have been developed to determine cell contractility on soft, optically transparent, deformable surfaces by quantifying substrate deformation in terms of cellular traction forces. However, the currently available tools that are used to study cell interactions are limited in their applicability due to the need for specialized technical expertise that is not amenable to the widespread adaptation of these techniques. Therefore, we have sought to develop a novel traction force microscopy technique known as micropattem traction microscopy. With this technique, we hope to greatly simplify the current traction force microscopy techniques and provide a method which will be able to be adopted by a wide range of laboratories. This dissertation describes the process ofthe development and application of this novel traction force technique to probe questions in mechanobiology that have not been previously broached due to the lack of appropriate tools. The technique itself uses indirect microcontact printing to create a regularized array of fluorescent protein onto a glass substrate, which is then transferred to an optically transparent, soft, elastic polyacrylamide hydrogel. Cells, limited by their ability to adhere only to patterned regions, will deform the pattern at these defined points. Thus, with knowledge of the bulk elastic properties ofthe substrate and a priori knowledge of the pattern, we are able to quantify the force a cell is exerting without its removal. We also developed and released a robust, automated MATLAB program that will aid users in the calculation of traction forces so that people with limited experience with programming can utilize the program without significant investments into training. This indirect approach allows for not only individual proteins, but also for multiple, spatially distinct, fluorescent proteins such as fibronectin and gelatin to be simultaneously patterned onto this surface as well. The ability to pattern multiple proteins in a spatially defmed region significantly aids in giving users control over as many parameters as possible. Finally, we will explore the current and future potential that this technique has to offer to researchers in the field of mechanobiology

Regulation of constitutive and alternative mRNA splicing across the human transcriptome by PRPF8 is determined by 5' splice site strength.

BACKGROUND: Sequential assembly of the human spliceosome on RNA transcripts regulates splicing across the human transcriptome. The core spliceosome component PRPF8 is essential for spliceosome assembly through its participation in ribonucleoprotein (RNP) complexes for splice-site recognition, branch-point formation and catalysis. PRPF8 deficiency is linked to human diseases like retinitis pigmentosa or myeloid neoplasia, but its genome-wide effects on constitutive and alternative splicing remain unclear. RESULTS: Here, we show that alterations in RNA splicing patterns across the human transcriptome that occur in conditions of restricted cellular PRPF8 abundance are defined by the altered splicing of introns with weak 5' splice sites. iCLIP of spliceosome components reveals that PRPF8 depletion decreases RNP complex formation at most splice sites in exon-intron junctions throughout the genome. However, impaired splicing affects only a subset of human transcripts, enriched for mitotic cell cycle factors, leading to mitotic arrest. Preferentially retained introns and differentially used exons in the affected genes contain weak 5' splice sites, but are otherwise indistinguishable from adjacent spliced introns. Experimental enhancement of splice-site strength in mini-gene constructs overcomes the effects of PRPF8 depletion on the kinetics and fidelity of splicing during transcription. CONCLUSIONS: Competition for PRPF8 availability alters the transcription-coupled splicing of RNAs in which weak 5' splice sites predominate, enabling diversification of human gene expression during biological processes like mitosis. Our findings exemplify the regulatory potential of changes in the core spliceosome machinery, which may be relevant to slow-onset human genetic diseases linked to PRPF8 deficiency

Conserved two-step regulatory mechanism of human epithelial differentiation

Human epithelia are organized in a hierarchical structure, where stem cells generate terminally differentiated cells via intermediate progenitors. This two-step differentiation process is conserved in all tissues, but it is not known whether a common gene set contributes to its regulation. Here, we show that retinoic acid (RA) regulates early human prostate epithelial differentiation by activating a tightly coexpressed set of 80 genes (e.g., TMPRSS2). Response kinetics suggested that some of these genes could be direct RA targets, whereas others are probably responding indirectly to RA stimulation. Comparative bioinformatic analyses of published tissue-specific microarrays and a large-scale transcriptomic data set revealed that these 80 genes are not only RA responsive but also significantly coexpressed in many human cell systems. The same gene set preferentially responds to androgens during terminal prostate epithelial differentiation, implying a cell-type-dependent interplay between RA and tissue-specific transcription factor-mediated signaling in regulating the two steps of epithelial differentiation

Medium-throughput processing of whole mount in situ hybridisation experiments into gene expression domains

This is the final version of the article. Available from the publisher via the DOI in this record.Understanding the function and evolution of developmental regulatory networks requires the characterisation and quantification of spatio-temporal gene expression patterns across a range of systems and species. However, most high-throughput methods to measure the dynamics of gene expression do not preserve the detailed spatial information needed in this context. For this reason, quantification methods based on image bioinformatics have become increasingly important over the past few years. Most available approaches in this field either focus on the detailed and accurate quantification of a small set of gene expression patterns, or attempt high-throughput analysis of spatial expression through binary pattern extraction and large-scale analysis of the resulting datasets. Here we present a robust, "medium-throughput" pipeline to process in situ hybridisation patterns from embryos of different species of flies. It bridges the gap between high-resolution, and high-throughput image processing methods, enabling us to quantify graded expression patterns along the antero-posterior axis of the embryo in an efficient and straightforward manner. Our method is based on a robust enzymatic (colorimetric) in situ hybridisation protocol and rapid data acquisition through wide-field microscopy. Data processing consists of image segmentation, profile extraction, and determination of expression domain boundary positions using a spline approximation. It results in sets of measured boundaries sorted by gene and developmental time point, which are analysed in terms of expression variability or spatio-temporal dynamics. Our method yields integrated time series of spatial gene expression, which can be used to reverse-engineer developmental gene regulatory networks across species. It is easily adaptable to other processes and species, enabling the in silico reconstitution of gene regulatory networks in a wide range of developmental contexts.The laboratory of Johannes Jaeger and this study in particular was funded by the MEC-EMBL agreement for the EMBL/CRG Research Unit in Systems Biology, by grant 153 (MOPDEV) of the ERANet: ComplexityNET program, by SGR grant 406 from the Catalan funding agency AGAUR, by grant BFU2009-10184 from the Spanish Ministry of Science, and by European Commission grant FP7-KBBE-2011-5/289434 (BioPreDyn)