Traditional Biocidal Replacement Viability of Microcrystalline Silver Chloride

The antimicrobial effects of silver ions and silver chloride nanoparticles have been well established while the efficacy of microcrystalline silver chloride has been less studied. Certex-AM, a microcrystalline silver chloride product produced by Cerion, Rochester, NY, was tested for its antimicrobial properties as a possible replacement for traditional biocidal techniques used in water cooling towers. The minimum inhibitory concentration (MIC) of the compound was determined using a microtiter broth assay. The compound was found to have inhibitory effects on bacterial growth for all tested organisms at concentrations greater than 9 ppm. Additional testing simulating a water cooling system showed the effectiveness of reducing an established wild population at concentrations of 10 ppm of the microcrystalline silver chloride. Certex-AM was found to be a promising replacement for traditional biocides as well as for other applications. Introduction of effective antimicrobial compounds such as this could reduce the pathogenic risk to humans associated with water cooling towers

Physiology From Anatomy Using Spatial Transcriptomic Mapping

Understanding the physiology of complex systems like tissues and organs is likely impossible without detailed structural maps of the anatomy, especially in the context of perturbations. Spatial transcriptomic techniques like Multiplexed Error Robust Fluorescence In Situ Hybridization or MERFISH have ushered in methods that are capable of generating these detailed anatomical maps for small regions of interest. Existing work primarily focuses on technological development, and few if any have compared perturbed to wild-type conditions. Here we present three cases of increasing difficulty where MERFISH can be used to compare a perturbed state to wild type. Existing spatial transcriptomic approaches, including MERFISH, lack the scale necessary to generate anatomical maps of large tissues and whole organs. Here we present Dimensionally Reduced Fluorescence In Situ Hybridization or dredFISH which allows the generation of detailed anatomical maps at scales far exceeding existing other approaches. Together the fundamental shift towards comparing biological conditions as well as the technological improvements in scale will provide a wealth of detailed anatomical maps which should provide unique physiological insights which likely would have been missed

Physiology From Anatomy Using Spatial Transcriptomic Mapping

Understanding the physiology of complex systems like tissues and organs is likely impossible without detailed structural maps of the anatomy, especially in the context of perturbations. Spatial transcriptomic techniques like Multiplexed Error Robust Fluorescence In Situ Hybridization or MERFISH have ushered in methods that are capable of generating these detailed anatomical maps for small regions of interest. Existing work primarily focuses on technological development, and few if any have compared perturbed to wild-type conditions. Here we present three cases of increasing difficulty where MERFISH can be used to compare a perturbed state to wild type. Existing spatial transcriptomic approaches, including MERFISH, lack the scale necessary to generate anatomical maps of large tissues and whole organs. Here we present Dimensionally Reduced Fluorescence In Situ Hybridization or dredFISH which allows the generation of detailed anatomical maps at scales far exceeding existing other approaches. Together the fundamental shift towards comparing biological conditions as well as the technological improvements in scale will provide a wealth of detailed anatomical maps which should provide unique physiological insights which likely would have been missed

Traditional Biocidal Replacement Viability of Microcrystalline Silver Chloride

The antimicrobial effects of silver ions and silver chloride nanoparticles have been well established while the efficacy of microcrystalline silver chloride has been less studied. Certex-AM, a microcrystalline silver chloride product produced by Cerion, Rochester, NY, was tested for its antimicrobial properties as a possible replacement for traditional biocidal techniques used in water cooling towers. The minimum inhibitory concentration (MIC) of the compound was determined using a microtiter broth assay. The compound was found to have inhibitory effects on bacterial growth for all tested organisms at concentrations greater than 9 ppm. Additional testing simulating a water cooling system showed the effectiveness of reducing an established wild population at concentrations of 10 ppm of the microcrystalline silver chloride. Certex-AM was found to be a promising replacement for traditional biocides as well as for other applications. Introduction of effective antimicrobial compounds such as this could reduce the pathogenic risk to humans associated with water cooling towers

scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.

MotivationSingle-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data.ResultsHere, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data.Availability and implementationThe R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997.Supplementary informationSupplementary data are available at Bioinformatics online

scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.

MotivationSingle-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data.ResultsHere, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data.Availability and implementationThe R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997.Supplementary informationSupplementary data are available at Bioinformatics online

OUP accepted manuscript

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. RESULTS: Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data. AVAILABILITY AND IMPLEMENTATION: The R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

ClipsMS: An Algorithm for Analyzing Internal Fragments Resulting from Top-Down Mass Spectrometry

Here we describe ClipsMS, an algorithm that can assign both terminal and internal fragments generated by top-down MS fragmentation. Further, ClipsMS can be used to locate various modifications on the protein sequence. Using ClipsMS to assign TD-MS generated product ions, we demonstrate that for apo-myoglobin, the inclusion of internal fragments increases the sequence coverage up to 78%. Interestingly, many internal fragments cover complimentary regions to the terminal fragments that enhance the information that is extracted from a single top-down mass spectrum. Analysis of oxidized apo-myoglobin using terminal and internal fragment matching by ClipsMS confirmed the locations of oxidation sites on the two methionine residues. Internal fragments can be beneficial for top-down protein fragmentation analysis, and ClipsMS can be a valuable tool for assigning both terminal and internal fragments present in a top-down mass spectrum.</p

ClipsMS: An Algorithm for Analyzing Internal Fragments Resulting from Top-Down Mass Spectrometry

Top-down mass spectrometry (TD-MS) of peptides and proteins results in product ions that can be correlated to polypeptide sequence. Fragments can either be terminal fragments, which contain either the N- or the C-terminus, or internal fragments that contain neither termini. Normally, only terminal fragments are assigned due to the computational difficulties of assigning internal fragments. Here we describe ClipsMS, an algorithm that can assign both terminal and internal fragments generated by top-down MS fragmentation. Further, ClipsMS can be used to locate various modifications on the protein sequence. Using ClipsMS to assign TD-MS generated product ions, we demonstrate that for apo-myoglobin, the inclusion of internal fragments increases the sequence coverage up to 78%. Interestingly, many internal fragments cover complementary regions to the terminal fragments that enhance the information that is extracted from a single top-down mass spectrum. Analysis of oxidized apo-myoglobin using terminal and internal fragment matching by ClipsMS confirmed the locations of oxidation sites on the two methionine residues. Internal fragments can be beneficial for top-down protein fragmentation analysis, and ClipsMS can be a valuable tool for assigning both terminal and internal fragments present in a top-down mass spectrum. Data are available via the MassIVE community resource with the identifiers MSV000086788 and MSV000086789