We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).

Abstract. The ability of cultured human fibroblasts to reorganize and contract three dimensional collagen I gels is regarded as an in vitro model for the reorganization of connective tissue during wound healing. We investigated whether adhesion receptors of the integrin family are involved . It was found that synthesis and transcription of the a2ß1 integrin (but not of a,ß, or a3ß1) is selectively upregulated when fibroblasts are seeded into type I collagen gels . Time course experiments revealed that high synthetic levels of a2ß, parallel the gel contraction process and return to "baseline" levels after the contraction has subsided . Furthermore, function-blocking mAbs directed to the a2 and ß, chain of integrins inhibited gel contraction.Remodelling of connective tissue can be important for tumor cells during invasion and formation of metastases . Therefore, we tested human melanoma cell T HE reorganization of collagen by fibroblasts is an important function in wound healing which leads to wound contraction and finally helps to reestablish organ integrity. The ability ofcultured fibroblasts to reorganize and contract three-dimensional collagen I gels (Bell et al., 1979) is considered as an in vitro model for wound contraction. Previous studies have described in detail the influence of cytokines (Gullberg et al., 1990), the requirement of protein synthesis and of an intact cytoskeleton for this process (Mauch, 1986 ; Guidry and Grinnell, 1985) . Seeding of fibroblasts into a three-dimensional collagen lattice results in major changes oftheir morphology (Tomasek et al., 1982), their protein and collagen metabolism (Mauch et al., 1988) as well as in their response to cytokines (Nagakawa et al., 1989). However, little is known, so far, about the role of extracellular matrix (ECM)1 receptors on the fibroblast surface for this function. Recently, evidence has been provided lines for this function . Five out of nine melanoma lines contracted collagen gels in vitro. Among these, two highly aggressive melanoma cell lines (MV3 and BLM) most efficiently contracted gels almost reaching the rate of normal adult fibroblasts. In these cells, synthesis of a2ß, was also significantly upregulated when seeded into collagen I gels . Moreover, function blocking anti-a2 in conjunction with anti-ßt chain mAbs completely inhibited gel contraction for several days.Other melanoma cells (530) with lower metastatic potential which were not able to contract gels, showed no induction of a2ß1 synthesis in gel culture. Our results suggest an important role of integrin a2ß, in the contraction of collagen I by normal diploid fibroblasts during wound healing and in the reorganization of collagen matrices by highly aggressive human melanoma cells.

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.

The accumulation of circulating low‐density neutrophils (LDN) has been described in cancer patients and associated with tumor‐supportive properties, as opposed to the high‐density neutrophils (HDN). Here we aimed to evaluate the clinical significance of circulating LDN in lung cancer patients, and further assessed its diagnostic vs prognostic value. Using mass cytometry (CyTOF), we identified major subpopulations within the circulating LDN/HDN subsets and determined phenotypic modulations of these subsets along tumor progression. LDN were highly enriched in the low‐density (LD) fraction of advanced lung cancer patients (median 7.0%; range 0.2%‐80%, n = 64), but not in early stage patients (0.7%; 0.05%‐6%; n = 35), healthy individuals (0.8%; 0%‐3.5%; n = 15), or stable chronic obstructive pulmonary disease (COPD) patients (1.2%; 0.3%‐7.4%, n = 13). Elevated LDN (>10%) remarkably related with poorer prognosis in late stage patients. We identified three main neutrophil subsets which proportions are markedly modified in cancer patients, with CD66b+/CD10low/CXCR4+/PDL1inter subset almost exclusively found in advanced lung cancer patients. We found substantial variability in subsets between patients, and demonstrated that HDN and LDN retain a degree of inherent spontaneous plasticity. Deep phenotypic characterization of cancer‐related circulating neutrophils and their modulation along tumor progression is an important advancement in understanding the role of myeloid cells in lung cancer.