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Abstract Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

The effects of varieties, producing areas, ears, and ear positions of maize on near-infrared (NIR) spectra were investigated to determine the factors causing the differences in NIR fingerprints of maize varieties. A total of 130 inbred lines were grown in two regions in China, and 12,350 kernel samples were analyzed through NIR spectroscopy. Spectral differences among varieties, producing areas, ears, and ear positions were determined and compared on the basis of pretreated spectra. The bands at 1300-1470, 1768-1949, 2010-2064, and 2235-2311 nm were mainly affected by the producing area. Band selection and principal component analysis were applied to improve the influence of variety on NIR spectra by processing the pretreated spectra. The degrees of the influence of varieties, producing areas, ears, and ear positions were calculated, and the percentages of the influence of varieties, producing areas, ears, and ear positions were 45.40%, 42.66%, 8.22%, and 3.72%, respectively. Therefore, genetic differences among maize inbred lines are the main factors accounted for NIR spectral differences. Producing area is a secondary factor. These results could provide a reference for researchers who authenticate varieties, perform geographical origin traceabilities, and conduct maize seed breeding.

False seeds can often be seen in the maize seed market, leading to a serious decline in maize yield. Those existing variety identification methods are expensive, time consuming, and destructive to seeds. The aim of this study is to develop a cheap, fast and non-destructive method which can robustly identify large amounts of maize seed varieties based on near-infrared reflectance spectroscopy (NIRS) and chemometrics. Because it is difficult to establish models for every variety in the market, this study mainly investigated the performance of models based on a large number of samples (more than 40 major varieties in the market). The reflectance spectra of maize seeds were collected by two modes (bulk kernels mode and single kernel mode). Both collection modes can be applied to identification, but only the single kernel mode can be applied to purity sorting. The spectra were pretreated with smoothing, the first derivative and vector normalization; and then principal component analysis (PCA), linear discriminant analysis (LDA) and biomimetic pattern recognition (BPR) were applied to establish identification models. The environmental factors such as producing areas and years have a significant influence on the performance of the models. Therefore, the method to improve the robustness of the models was investigated in this study. New indexes (correct acceptance degree (CAD), correct rejection degree (CRD) and correct degree (CD)) were defined to analyze the performance of the models more accurately. Finally, the models obtained a mean correct discrimination rate of over 90%, and exhibited robust properties for samples harvested from different areas and years. The results showed that NIR technology combined with chemometrics methods such as PCA, LDA, and BPR could be a suitable and alternative technique to identify the authenticity of maize seed varieties. [ABSTRACT FROM AUTHOR]