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. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.

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. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

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. Here, we developed a computational framework named TimeTalk from integrated public time-course mouse scRNA-seq datasets to decipher the secret of eLRs. Extensive validations and analyses were performed to ensure the involvement of identified eLRs in early embryo development. Process analysis identified that eLRs could be divided into six temporal windows corresponding to sequential events in the early embryo development process. With the interpolation strategy, TimeTalk is powerful in revealing paracrine settings and studying cell-cell communication during early embryo development. Furthermore, by using TimeTalk in the blastocyst and blastoid models, we found that the blastoid models share the core communication pathways with the epiblast and primitive endoderm lineages in the blastocysts. This result suggests that TimeTalk has transferability to other bio-dynamic processes. We also curated eLRs recognized by TimeTalk, which may provide valuable clues for understanding early embryo development and relevant disorders.

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. Applying SCALE to the "Mouse Aging Cell Atlas" (Tabula Muris Senis) data, we identified tissue-level transcriptomic aging programs for more than 20 murine tissues and created a multitissue resource of mouse quantitative aging-associated genes. We observe that SCALE correlates well with other age indicators, such as the accumulation of somatic mutations, and can distinguish subtle differences in aging even in cells of the same chronological age. We further compared SCALE with other transcriptomic and methylation "clocks" in data from aging muscle stem cells, Alzheimer's disease, and heterochronic parabiosis. Our results confirm that SCALE is more generalizable and reliable in assessing biological aging in aging-related diseases and rejuvenating interventions. Overall, SCALE represents a valuable advancement in our ability to measure aging accurately, robustly, and interpretably in single cells.

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. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

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)°]. In the crystal, O-H⋯O and N-H⋯O hydrogen bonds link the mol-ecules, generating (110) sheets.

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. deviation being 0.0087 Å. In the crystal, the partially overlapped arrangement and the face-to-face distance of 3.466 (17) Šbetween parallel pyridine rings of neighboring mol-ecules indicates the existence of π-π stacking. Inter-molecular O-H⋯N hydrogen bonding and weak C-H⋯O hydrogen bonding are present in the crystal structure.

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.

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) Šout of the basal plane toward the water mol-ecule. The asymmetric unit is completed by two solvent water mol-ecules. These water mol-ecules participate in the formation of an intricate three-dimensionnal network of hydrogen bonds involving the coordinated water mol-ecule and the NH groups.

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):0.48 (3) and 0.63 (3):0.37 (3), respectively]. In the crystal, inter-molecular N-H⋯O hydrogen bonds connect the complex cations into a zigzag chain along [010] and further N-H⋯O hydrogen bonds between the chains and the uncoordinated nitrate anions lead to layers parallel to (100).