Somatic Genomic and Transcriptomic Changes in Single Ischemic Human Heart Cardiomyocytes.

Heart failure is a multifaceted syndrome contributing significantly to mortality and hospitalization rates among the global population. One of the prevalent causes of heart failure is ischemic heart disease (IHD), often caused by a blockage in a coronary artery, ultimately leading to the loss of myocardial tissue and contractile force. The impact of this ischemic ambiance on the cardiomyocyte genome and transcriptome has not been thoroughly studied.

Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data.

Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice.

MAPS: pathologist-level cell type annotation from tissue images through machine learning.

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data.

TimeTalk uses single-cell RNA-seq datasets to decipher cell-cell communication during early embryo development.

Early embryonic development is a dynamic process that relies on proper cell-cell communication to form a correctly patterned embryo. Early embryo development-related ligand-receptor pairs (eLRs) have been shown to guide cell fate decisions and morphogenesis. However, the scope of eLRs and their influence on early embryo development remain elusive.

A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures.

Accurately measuring biological age is crucial for improving healthcare for the elderly population. However, the complexity of aging biology poses challenges in how to robustly estimate aging and interpret the biological significance of the traits used for estimation. Here we present SCALE, a statistical pipeline that quantifies biological aging in different tissues using explainable features learned from literature and single-cell transcriptomic data.

MAPS: Pathologist-level cell type annotation from tissue images through machine learning.

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data.

3-Carb-oxy-methyl-1H-indole-4-carb-oxy-lic acid.

In the title compound, C(11)H(9)NO(4), the carboxyl group bonded to the six-membered ring lies close to the plane of the 1H-indole ring system [dihedral angle = 13.13 (9)°], whereas the carb-oxy-lic acid group linked to the five-membered ring by a methyl-ene bridge is close to perpendicular [78.85 (9)°].

6-Methyl-nicotinic acid.

All non-H atoms of the title compound, C(7)H(7)NO(2), are nearly coplaner, the r.m.s.

5-Methyl-2-pyridone.

The crystal structure of the title compound, C(6)H(7)NO, is stabilized by inter-molecular N-H⋯O hydrogen bonds, resulting in inversion dimers. The structure is further consolidated by weak C-H⋯O hydrogen bonds.

Dichlorido[bis-(2-ethyl-5-methyl-1H-imidazol-4-yl-κN)methane]-cobalt(II) monohydrate.

In the title compound, [CoCl(2)(C(13)H(20)N(4))]·H(2)O, the Co(II) atom lies on a mirror plane and is four-coordinated by two N atoms of the imidazole ligand and two Cl atoms in a distorted tetra-hedral arrangement. The water mol-ecule participates in the formation of hydrogen bonds, resulting in a three dimensional network involving the Cl atoms and the NH groups. The terminal C atom of the ethyl group is disordered over two sites of equal occupancy.

Bis[bis-(2-ethyl-5-methyl-1H-imidazol-4-yl-κN)methane](nitrato-κO,O')nickel(II) nitrate.

In the title compound, [Ni(NO(3))(C(13)H(20)N(4))(2)]NO(3), the Ni(II) ion shows a distorted octa-hedral geometry formed by four N atoms from two bis-(2-ethyl-5-methyl-1H-imidazol-4-yl)methane ligands and two O atoms from a chelating nitrate anion. Three ethyl groups in the complex cation and the O atoms of the uncoordinated nitrate anion are disordered over two sets of positions [occupancy ratios of 0.52 (3):0.

Aqua-[bis-(2-ethyl-5-methyl-1H-imidazol-4-yl-κN)methane]-oxalatocopper(II) dihydrate.

In the title compound, [Cu(C(2)O(4))(C(13)H(20)N(4))(H(2)O)]·2H(2)O, the Cu(II) atom exhibits a distorted square-pyramidal geometry with the two N atoms of the imidazole ligand and the two O atoms of the oxalate ligand forming the basal plane, while the O atom of the coordinated water mol-ecule is in an apical position. The Cu(II) atom is shifted 0.232 (2) Å out of the basal plane toward the water mol-ecule.