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Biology subjects

Kumar, T.

Publications and source records attributed to Kumar, T..

At least 19 recordsLinked to original sources

Massive inter-animal horizontal gene transfer contributes to stress tolerance in the fungus gnat Bradysia coprophila

Horizontal gene transfer (HGT) in animals is thought to be rare and largely restricted to transfers from non-animal sources. In contrast to this expectation, we discovered that 9.2% of the protein-coding genes in the genome of the dark-winged fungus gnat Bradysia coprophila (order: Diptera, family: Sciaridae) are of potential HGT origin. Most of these genes have phylogenetic affinity with sequences from other distantly related arthropods that inhabit a shared soil-dwelling niche with B. coprophila. We show that these findings are inconsistent with genomic contamination or widespread gene loss and instead suggest a level of inter-animal HGT that is, to our knowledge, unprecedented. Consistent with their potential functional importance, ~75% of putative B. coprophila horizontally transferred genes (HTGs) are transcribed, and nearly all are shared across multiple sciarid fungus gnat species that diverged tens of millions of years ago. Further, domain annotations suggest that HTGs participate in diverse processes potentially important to B. coprophila ecology. We tested the functions of eight putative B. coprophila HTGs through transgenic expression in the fly Drosophila melanogaster and the yeast Saccharomyces cerevisiae, and found evidence that HGT has likely endowed B. coprophila with the novel capability to biosynthesize NAD+, as well as enhanced capacity to withstand stressors imposed by arsenic, cyanide, alkylating agents, and reactive oxygen species. Our findings reveal a previously underappreciated contribution of inter-animal genetic exchange to the evolution of genomic, physiological, and ecological novelty.

evolutionary biology↗

The Hox code abolishes anatomical variation in an insect gonad

Phenotypic traits can vary based on genes or environment. Nevertheless, some quantitative traits remain invariant. Understanding the developmental mechanisms that generate such invariance can reveal how phenotypic robustness evolves. Insect reproductive capacity is largely determined by the number of gamete-producing subunits within the gonad, providing a tractable system to investigate these mechanisms as they operate on a quantitative trait that impacts fitness. In most insects, gonad subunit number varies within and between species. Here, we investigate gonad development in the milkweed bug Oncopeltus fasciatus, wherein subunit number is invariant. We show that subunit number is established during embryogenesis, when some abdominal segments, but not others, specify somatic gonad precursors. The position and number of these precursors depend on segment identity, and experimentally modified segment number or Hox-mediated segment identity changes the number of gonad subunits. Thus, O. fasciatus establishes invariant subunit number by coupling gonad development to segmental patterning machinery. We propose that co-option of the Hox code provides a developmental mechanism for generating robustness in gonad subunit number and may thereby contribute to the evolution of reproductive capacity in other insects.

developmental biology↗

Comparative analysis of immunomodulatory effects of Artemisinin and Parthenolide on activation of immune cells and their roles in Salmonella Typhimurium infection

Plant-derived immunomodulators are of immense therapeutic interest, of which sesquiterpene lactones (SLs) are a promising source. Artemisinin and Parthenolide are two structurally related SLs that are known to inhibit NF-{kappa}B. However, whether this mechanism produces comparable immunomodulatory outcomes has not been directly examined. In this study, we compared the effects of Artemisinin and Parthenolide across different in vitro and in vivo inflammatory contexts: T-cell activation, macrophage activation, and Salmonella Typhimurium infection. Parthenolide inhibited mouse T-cell activation more effectively than Artemisinin in terms of cell cycling, IL-2 production, and induction of activation markers, CD69 and CD44. In addition, Parthenolide suppressed production of LPS-induced nitrite and pro-inflammatory cytokines in primary thioglycolate (TG)-elicited macrophages as well as RAW 264.7 cells. It also reduced ROS production across all tested conditions. In contrast, Artemisinin exhibited comparatively modest effects, although it increased IL-6 in LPS-activated RAW 264.7 cells. Neither compound reduced bacterial burden in an in vitro model of S. Typhimurium infection; however, Parthenolide, but not Artemisinin, lowered TNF- amounts. Together, these findings demonstrate that Parthenolide is a broader and more consistent immunomodulator than Artemisinin. These observations led us to investigate the effects of Parthenolide in an in vivo model of mice orally fed with S. Typhimurium. Parthenolide-treated mice showed higher survival accompanied with lower sera amounts of IL-6 and TNF-, indicating that its protective effects operate by limiting host immunopathology. The implications of these observations on the use of compounds that lower host inflammatory responses during infections are discussed.

immunology↗

A Comprehensive Benchmarking of Spatial Deconvolution andDomain Detection Methods across Diverse Tissues and SpatialTranscriptomic Technologies

Spatial transcriptomic technologies enable high-resolution characterization of gene expression patterns and reconstruction of cellular architecture within tissue contexts. Two key computational problems have emerged for analyzing these datasets: spatial deconvolution, for disentangling cell-type compositions at spatial locations, and spatial domain detection, for identifying spatially coherent regions within a tissue section. Although numerous methods have been developed for each task, a comprehensive and unified benchmarking study spanning diverse tissue types, spatial resolutions, and technological platforms remains lacking, hindering informed method selection by end users and impeding future methodological advancements. Here, we present spDDB (https://github.com/Zafar-Lab/spDDB), a comprehensive benchmarking framework for spatial deconvolution and domain detection methods across a large and diverse collection of datasets spanning multiple tissues, technologies, and biological conditions. We evaluated 21 deconvolution methods, including seven recently-developed approaches, across 37 datasets curated from brain, cancer, and organ tissues encompassing four distinct technologies. To enable rigorous evaluation, we introduced SynthST, a deep graph attention autoencoder-based simulator that generates realistic spatial cell-type distributions from spatial transcriptomic data, and employed a suite of spatial bivariate metrics including a novel bivariate Gearys C metric, alongside rare cell-type, and cell-shape characterization metrics, for multidimensional performance assessment. While Cell2location, RCTD and SONAR emerged as top-performing methods for spatial deconvolution across tissue types, deconvolution performance varied substantially based on tissue architecture, spatial technology, dataset scale, and cell type diversity. For domain detection, we benchmarked 18 methods across 36 datasets spanning six spatial technologies, identifying PROST, BASS, and SpaceFlow as the leading approaches, while revealing notable limitations of existing methods in handling large-scale datasets. Finally, we provide practical guidelines to assist end users in selecting optimal methods for both tasks across diverse experimental settings.

bioinformatics↗

Dual apical methyltransferases orchestrate motility initiation in apicomplexan parasites

Apicomplexan parasites such as Toxoplasma gondii initiate motility through rapid, spatially confined cytoskeletal activation at their apical end. While calcium-, lipid-, and kinase-based signalling pathways have been partially elucidated, how these cues are translated into mechanical force remains unclear. Here, we uncover a dual methyltransferase mechanism that orchestrates this process. We identify PCKMT, a preconoidal ring-associated lysine methyltransferase, as an essential upstream regulator of motility. PCKMT anchors the actin nucleator Formin-1 (FRM1) at the conoid, enabling conoid protrusion and F-actin assembly. Loss of PCKMT abolishes FRM1 recruitment, blocks conoid extrusion, and arrests invasion and egress despite preserved conoid structure. In contrast, the apical methyltransferase AKMT, previously linked to motility through recruitment of the glideosome-associated connector (GAC), acts downstream, disengaging from the conoid upon activation and likely promoting GAC-dependent force transmission. PCKMT depletion prevents AKMT translocation, revealing that actin assembly and lysine methylation are mechanistically coupled. Together, these findings define a two-step methylation checkpoint that coordinates actin nucleation with force propagation, uncovering methylation as a central regulatory axis for motility initiation in apicomplexan parasites.

microbiology↗

Translating clinical gene sequencing into a foundational representation of tumor subtype

While gene sequencing is routine in cancer care, translating sequences into treatment decisions remains a challenge. Here we introduce MutationProjector, an AI foundation model that transforms tumor mutation profiles into a compact representation of cancer subtype, with broad implications for diagnosis and therapy. MutationProjector is pre-trained by integrating genomic alterations from >30,000 tumors with extensive molecular knowledge, yielding a model that accurately reconstructs held-out genetic profiles (demonstrating strong generalization) and determines subtype representations from altered molecular pathways (enabling model interpretability). We evaluate MutationProjector in independent tasks related to prediction of immunotherapy response, prediction of chemotherapy response, and classification of metastasis, recording leading performance in all areas. Each task identifies key biomarkers of interest, including KMT2A and KRAS-STK11 alterations which govern immunotherapy response.

systems biology↗

Chinmo is a novel regulator of differential Hippo signaling response within a single developing organ.

Orchestrated control of proliferation of multiple cell types is essential for building a healthy organ. Here we use larval ovary development in Drosophila melanogaster as a model to understand the homeostasis of somatic and germ line cells in the formation of the female adult reproductive organ. We previously showed that the highly conserved Hippo signaling pathway regulates proliferation of both germ line and somatic cells in the D. melanogaster larval ovary. Response to Hippo signaling appeared to be mediated by different genetic mechanisms in germ line and soma, but the mechanism allowing distinct responses to the same signaling pathway remained unknown. Here we perform cell type-specific RNA-Seq of isolated germ line and somatic cells from the developing ovary at multiple time points spanning the formation of germ line stem cell niches, in either a Hippo signaling loss- or gain-of-function genetic background. Applying network analysis to these data revealed a novel regulator of ovarian development, the transcription factor chinmo. Subsequent experimental validation showed that chinmo acts as a key germ cell-specific translator of Hippo signaling in the developing ovary, allowing the Hippo signal to be transduced in cell type-specific ways in germ line and somatic cells within the same organ.

developmental biology↗

High intracellular calcium amounts inhibit activation-induced proliferation of mouse T cells

Optimal T cell activation is critical to orchestrate adaptive immune responses. Calcium is critical for T cell activation and integrates signaling pathways necessary to activate key transcription factors. In fact, patients with calcium channelopathies are immunodeficient. Here, we investigated the effects of different concentrations of intracellular calcium on activation of mouse T cells. High intracellular calcium amounts inhibited in vitro T cell proliferation as evidenced by a decreased cell cycling-to-hypodiploidy ratio in two models of activation: the combination of phorbol 12-myristate 13-acetate (PMA) and Ionomycin (an ionophore)/Thapsigargin (a SERCA inhibitor) or plate bound anti-CD3 and anti-CD28. High intracellular calcium amounts increased the production of reactive oxygen species (ROS) in T cells activated with PMA and Ionomycin and scavenging excess ROS using N-acetyl cysteine (NAC) or PEGylated superoxide dismutase (PEG-SOD) rescued the decrease in cycling-to-hypodiploidy ratio. To test the universality of our observations, we studied the effects of tert-Butylhydroquinone (tBHQ), a SERCA inhibitor and Nrf2 activator. tBHQ alone did not increase intracellular calcium amounts but increase was observed along with PMA. Also, tBHQ inhibited T cell activation in a dose-dependent manner in both in vitro models of T cell activation. Importantly, intraperitoneal injection of tBHQ ameliorated Dextran Sodium Sulfate (DSS)-induced colitis in mice as evidenced by rescue of colon length shortening and lower disease activity index. Overall, this study identifies high calcium amounts as a potential target to lower T cell activation. The implications of these observations are discussed as this strategy may be important in treatment of some autoimmune diseases.

immunology↗

Haplo-pheno association for OsNRT1.1 paralog in rice reveals superior haplogroup with high nitrate uptake efficiency

Haplotype-based breeding approaches hold promise for enhancing crop improvement strategies, allowing for targeted selection of superior genetic combinations to develop high-yielding and resilient varieties. The current study aimed at identification of NRT1.1 nitrate transporter haplotype that could serve as "donors" in haplotype-based breeding. We phenotyped 272 rice accessions in hydroponics with sufficient and low nitrogen (N) for nitrate uptake efficiency. By employing principal component and hierarchical cluster analysis, the accessions were grouped into N efficient, intermediate, and inefficient clusters. Haplotype analysis unveiled the presence of two haplogroups for OsNRT1.1A, three for OsNRT1.1B, and five for OsNRT1.1C. Through haplo-pheno association, the comparison of mean trait values revealed H2 and H3 as the superior haplotypes (SH) for OsNRT1.1A and OsNRT1.1B, respectively. In the case of OsNRT1.1C, H3 and H1 emerged as SH within the N-efficient cluster. Conversely, the inferior haplotypes (IH) consisted of H1 in OsNRT1.1A, H3 in OsNRT1.1B, and H3 and H2 in OsNRT1.1C within the N-inefficient cluster. However, relative expression of OsNRT1.1 (with specific paralogs) in contrasting rice accessions revealed that a few of the inferior accession exhibited higher expression levels in the root but lower in the shoot, which might have contributed to their N-inefficiency. Furthermore, amino acid change at position 403 (Isoleucine to Valine) in inferior accessions influences the active site OsNRT1.1C protein causing N-inefficiency. Ours is the first report on haplotype analysis of NRT1.1 gene demonstrating its genetic diversity, as well as its association with phenotype will have potential implications for improving nitrate uptake efficiency.

plant biology↗

A spatially resolved single cell genomic atlas of the adult human breast

The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.

genomics↗

Time of Sample Collection Critical for Microbiome Replicability

Although many aspects of microbiome studies have been standardized to improve experimental replicability, none account for how the daily diurnal fluctuations in the gut lumen cause dynamic changes in 16S amplicon sequencing. Here we show that sample collection time affects the conclusions drawn from microbiome studies and are larger than the effect size of a daily experimental intervention or dietary changes. The timing of divergence of the microbiome composition between experimental and control groups are unique to each experiment. Sample collection times as short as only four hours apart lead to vastly different conclusions. Lack of consistency in the time of sample collection may explain poor cross-study replicability in microbiome research. Without looking at other data, the impact on other fields is unknown but potentially significant. One-Sentence SummaryIf we are not controlling for host circadian rhythm time in microbiome studies when performing experiments, it is like trying to measure sea level rise while not knowing that tides or waves exist.

systems biology↗

Effects of Variation in Urine Sample Storage Conditions on 16S Urogenital Microbiome Analyses

Replicability is a well-established challenge in microbiome research with a variety of contributing factors at all stages, from sample collection to code execution. Here, we focus on voided urine sample storage conditions for urogenital microbiome analysis. Using urine samples collected from 10 healthy adult women, we investigated the microbiome preservation efficacy of AssayAssure(R) Genelock (as opposed to no preservative) under different temperature conditions. We varied temperature over 48 hours in order to examine the impact of conditions samples may experience with home voided urine collection and shipping to a central biorepository. The following common lab and shipping conditions were investigated: -20C, ambient temperature, 4C, a freeze-thaw cycle, and a heat cycle. At 48 hours, all samples were stored at -80C until processing. After generating 16S rRNA gene amplicon sequencing data using the highly sensitive KatharoSeq protocol, we observed individual variation in both alpha and beta diversity metrics below interhuman differences, corroborating reports of individual microbiome variability in other specimen types. While there was no significant difference in beta diversity when comparing AssayAssure(R) Genelock vs. no preservative, we did observe a higher concordance with AssayAssure samples shipped at colder temperatures (-20C and 4C) when compared to the samples shipped at -20C without preservative. Our results indicate that AssayAssure does not introduce a significant amount of microbial bias when used on a range of temperatures but is most effective at colder temperatures. ImportanceThe urogenital microbiome is an understudied yet important human microbiome niche. Research has been stimulated by the relatively recent discovery that urine is not sterile: urinary tract microbes have been linked to health problems including urinary infections, incontinence, and cancer. The quality of life and economic impact of UTIs and urgency incontinence alone are enormous, with $3.5 billion and $82.6 billion respectively spent in the U.S. annually. Given the low biomass of urine, novelty of the field, and well-established replicability bias in microbiome studies, it is critical to study storage conditions on urine samples to minimize microbial biases. Efficient and reliable preservation methods permit home self-sample collection and shipping, increasing the accessibility of larger-scale studies. Here, we examined both buffer and temperature variation effects on 16S rRNA gene amplicon sequencing results from urogenital samples, providing data on the consequences of common storage methods on urogenital microbiome results.

microbiology↗

MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication

With the evolution of multicellularity, communication among cells in different organs/tissues became pivotal to life. Molecular basis of such communication has long been studied, but genome-wide screens for biomolecules/genes mediating tissue-tissue signaling are lacking. To systematically identify inter-tissue mediators, we present a novel computational approach MultiCens (Multilayer/Multi-tissue network Centrality measures). Unlike single-layer network methods, MultiCens can distinguish within- vs. across-layer connectivity to quantify the "influence" of any gene in a tissue on a query set of genes of interest in another tissue. MultiCens enjoys theoretical guarantees on convergence and decomposability, and excels on synthetic benchmarks. On human multi-tissue datasets, MultiCens predicts known and novel genes linked to hormones. MultiCens further reveals shifts in gene network architecture among four brain regions in Alzheimers disease. MultiCens-prioritized hypotheses from these two diverse applications, and potential future ones like "Multi-tissue-expanded Gene Ontology" analysis, can enable whole-body yet molecular-level investigations in humans.

bioinformatics↗

A conserved Guided Entry of Tail-anchored pathway is involved in the trafficking of tail-anchored membrane proteins in Plasmodium falciparum.

Tail-anchored (TA) proteins are defined by the absence of N-terminus signal sequence and the presence of a single transmembrane domain (TMD) proximal to their extreme C-terminus. They play fundamental roles in cellular processes including vesicular trafficking, protein translocation and quality control. Accordingly, TA proteins are post-translationally integrated by the Guided Entry of TA (GET) pathway to the cellular membranes; with their N-terminus oriented towards the cytosol and C-terminus facing the organellar lumen. The TA repertoire and the GET machinery have been extensively characterized in the yeast and mammalian systems, however, they remain elusive in the human malaria parasite Plasmodium falciparum. In this study, we bioinformatically predicted a total of 63 TA proteins in the P. falciparum proteome and revealed the association of their subset with the P. falciparum homolog of Get3 (PfGet3). In addition, our proximity labelling studies either definitively identified or shortlisted the other eligible GET constituents, and our in vitro association studies validated associations between PfGet3 and the corresponding homologs of Get4 and Get2 in P. falciparum. Collectively, this study reveals the presence of proteins with hallmark TA signatures and the involvement of evolutionary conserved GET trafficking pathway for their targeted delivery within the parasite. SynopsisTail-anchored (TA) proteins, characterized by an absence of N-terminal signal sequence and the presence of a transmembrane domain near the C-terminus, are post-translationally inserted at organellar membranes by the conserved multi-component Guided Entry of TA (GET) pathway. Here, we identified the putative homologs of GET machinery in the human malaria parasite Plasmodium falciparum and revealed their association with a subset of bioinformatically predicted 63 putative TA proteins, thereby validating the functional existence of this trafficking pathway within the apicomplexan parasite. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/442402v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1c0239org.highwire.dtl.DTLVardef@d5ffbaorg.highwire.dtl.DTLVardef@1884a50org.highwire.dtl.DTLVardef@c6df7d_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

Optofluidic Fiber Component for Separation and counting of Micron-Sized Particles

An all-fiber separation component capable of sorting and counting micron-sized particles based on size is presented. A sequence of silica fiber capillaries with various diameters and longitudinal cavities were used to fabricate the component for separation and detection in an uninterrupted flow. Fluorescence microparticles of 1 m and 10 m sizes are mixed in a visco-elastic fluid and infused into the all-fiber separation component. Elasto-inertial forces focus the larger particle to the center of the silica capillary, while the smaller microparticles exit from a side capillary. Analysis of the separated particles at the output showed a separation efficiency of 100% for the 10 m and 97% for the 1 m particles. In addition, the counting of the larger particles is demonstrated in the same flow. The separated 10 m particles are further routed through another all-fiber component for counting. A counting speed of ~1400 particles/min and with the variation in amplitude of 10% is achived. A combination of separation and counting can be powerful tool may find several applications in biology and medicine, such as separation and analysis of exosomes, bacteria, and blood cell sub-populations.

bioengineering↗

Predicting cross-tissue hormone-gene relations usingbalanced word embeddings

MotivationLarge volumes of biomedical literature present an opportunity to build whole-body human models comprising both within-tissue and across-tissue interactions among genes. Current studies have mostly focused on identifying within-tissue or tissue-agnostic associations, with a heavy emphasis on associations among disease, genes and drugs. Literature mining studies that extract relations pertaining to inter-tissue communication, such as between genes and hormones, are solely missing. ResultsWe present here a first study to identify from literature the genes involved in inter-tissue signaling via a hormone in the human body. Our models BioEmbedS and BioEmbedS-TS respectively predict if a hormone-gene pair is associated or not, and whether an associated gene is involved in the hormones production or response. Our models are classifiers trained on word embeddings that we had carefully balanced across different strata of the training data such as across production vs. response genes of a hormone (or) well-studied vs. poorly-represented hormones in the literature. Model training and evaluation are enabled by a unified dataset called HGv1 of ground-truth associations between genes and known endocrine hormones that we had compiled. Our models not only recapitulate known gene mediators of tissue-tissue signaling (e.g., at average 70.4% accuracy for BioEmbedS), but also predicts novel genes involved in inter-tissue communication in humans. Furthermore, the species-agnostic nature of our ground-truth HGv1 data and our predictive modeling approach, demonstrated concretely using human data and generalized to mouse, hold much promise for future work on elucidating inter-tissue signaling in other multi-cellular organisms. AvailabilityProposed HGv1 dataset along with our models predictions, and the associated code to reproduce this work are available respectively at https://cross-tissue-signaling.herokuapp.com/, and https://github.com/BIRDSgroup/BioEmbedS. Contactnmanik@cse.iitm.ac.in

bioinformatics↗

High resolution and high throughput bacteria separation from blood using elasto-inertial microfluidics

Improved sample preparation has the potential to address a huge unmet need for fast turnaround sepsis tests that enable early administration of appropriate antimicrobial therapy. In recent years, inertial and elasto-inertial microfluidics-based sample preparation has gained substantial interest for bioparticle separation applications. However, for applications in blood stream infections the throughput and bacteria separation efficiency has thus far been limited. In this work, for the first time we report elasto-inertial microfluidics-based bacteria isolation from blood at throughputs and efficiencies unparalleled with current microfluidics-based state of the art. In the method, bacteria-spiked blood sample is prepositioned close to the outer wall of a spiral microchannel using a viscoelastic sheath buffer. The blood cells will remain fully focused throughout the length of the channel while bacteria migrate to the inner wall for effective separation. Initially, particles of different sizes were used to investigate particle focusing and the separation performance of the spiral device. A separation efficiency of 96% for the 1 {micro}m particles was achieved, while 100% of 3 {micro}m particles were recovered at the desired outlet at a high throughput of 1 mL/min. Following, processing blood samples revealed a minimum of 1:2 dilution was necessary to keep the blood cells fully focus at the outer wall. In experiments involving bacteria spiked in diluted blood, viable E.coli were continuously separated at a total flow rate of 1 mL/min, with an efficiency between 82 to 90% depending on the blood dilution. Using a single spiral, it takes 40 minutes to process 1 mL of blood at a separation efficiency of 82% and 3 hours at 90% efficiency. To the best of our knowledge, this is the highest blood sample throughput per single microfluidic chip reported for the corresponding separation efficiency. As such, the label-free, passive and high throughput bacteria isolation method has a great potential for speeding up downstream phenotypic and molecular analysis of bacteria.

biophysics↗

Fluorescence Microscopy Datasets for Training Deep Neural Networks

BackgroundFluorescence microscopy is an important technique in many areas of biological research. Two factors which limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging, and, when imaging live cells, phototoxicity caused by light exposure. Recently developed methods in machine learning are able to greatly improve the signal to noise ratio of acquired images. This allows researchers to record images with much shorter exposure times, which in turn minimizes photobleaching and phototoxicity by reducing the dose of light reaching the sample. FindingsTo employ deep learning methods, a large amount of data is needed to train the underlying convolutional neural network. One way to do this involves use of pairs of fluorescence microscopy images acquired with long and short exposure times. We provide high quality data sets which can be used to train and evaluate deep learning methods under development. ConclusionThe availability of high quality data is vital for training convolutional neural networks which are used in current machine learning approaches.

bioinformatics↗