Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex.

Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.

Computational Approaches and Challenges in Spatial Transcriptomics.

The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding.

Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here we propose Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of reference single-cell RNA-seq data and the spatial information of spatially resolved transcriptomics data. We present a series of benchmarking analyses on publicly available spatially resolved transcriptomics datasets, that demonstrate the superiority of Spatial-ID compared with state-of-the-art methods. Besides, we apply Spatial-ID on a self-collected mouse brain hemisphere dataset measured by Stereo-seq, that shows the scalability of Spatial-ID to three-dimensional large field tissues with subcellular spatial resolution.

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.

Comparison of IOL Power Calculation Formulas for a Trifocal IOL in Eyes With High Myopia.

PURPOSE: To analyze the results of new intraocular lens (IOL) formulas (Emmetropia Verifying Optical [EVO], Kane, Olsen, and Barrett Universal II), traditional formulas (Haigis and SRK/T), and modified Wang-Koch axial length adjustment formulas with the SRK/T and Holladay 1 (SRK/Tmodified-W/K and H1modified-W/K) in Chinese patients with long eyes. METHODS: In this retrospective case series, patients with an axial length of 26 mm or greater having uneventful femtosecond laser-assisted cataract surgery with one trifocal IOL model were enrolled. The actual postoperative spherical equivalent of the manifest refraction was compared with the formula-predicted refraction based on the implanted IOL power. A subgroup analysis was performed based on the axial length. RESULTS: A total of 113 eyes was enrolled. Using User Group for Laser Interference Biometry constants, the modified Wang-Koch formulas had the lowest percentage of eyes with hyperopic outcomes. The Barrett Universal II, Olsen, Kane, and EVO 2.0 formulas produced a statistically lower median absolute error than the SRK/Tmodified-W/K and SRK/T formulas (P < .05). The Barrett Universal II formula produced higher percentages of eyes within ±0.50 diopters (D) of the prediction error than the SRK/T formula (P < .05). In eyes with axial lengths of less than 28 mm, there were no significant differences in the prediction accuracy of the eight formulas. In eyes with axial lengths of 28 mm or greater, the new IOL formulas yielded the lowest median absolute error, followed by the H1modified-W/K and Haigis formulas. The SRK/Tmodified-W/K formula had the highest mean absolute error and the lowest percentages of eyes within ±0.25 and ±0.50 D of endpoint. The traditional formulas yielded the highest risk of refractive surprise. CONCLUSIONS: All formulas achieved good results in eyes with axial lengths of less than 28 mm with trifocal IOL implanted. The newer formulas tend to produce better outcomes for eyes with high myopia. The SRK/Tmodified-W/K formula provided improved accuracy only in eyes with axial lengths of 30 mm or greater. [J Refract Surg. 2021;37(8):538-544.].