Brain extraction is an important preprocessing step for further processing (e.g., registration and morphometric analysis) of brain MRI data. Due to the operator-dependent and time-consuming nature of manual extraction, automated or semi-automated methods are essential for large-scale studies. Automatic methods are widely available for human brain imaging, but they are not optimized for rodent brains and hence may not perform well. To date, little work has been done on rodent brain extraction. We present an extended pulse-coupled neural network algorithm that operates in 3-D on the entire image volume. We evaluated its performance under varying SNR and resolution and tested this method against the brain-surface extractor (BSE) and a level-set algorithm proposed for mouse brain. The results show that this method outperforms existing methods and is robust under low SNR and with partial volume effects at lower resolutions. Together with the advantage of minimal user intervention, this method will facilitate automatic processing of large-scale rodent brain studies.

Assays that can determine the response of tumor cells to cancer therapeutics could greatly aid the selection of drug regimens for individual patients. However, no functional assays are currently implemented clinically, and predictive genetic biomarkers are available for only a small fraction of cancer therapies. Here we demonstrate that the single-cell mass accumulation rate (MAR), profiled over many hours with a suspended microchannel resonator, accurately defines the drug sensitivity or resistance of glioblastoma multiforme (GBM) and B-cell acute lymphocytic leukemia (B-ALL) cells. MAR reveals heterogeneity in drug sensitivity not only between different tumors but also within individual tumors and tumor-derived cell lines. MAR measurement predicts drug response using samples as small as 25 μL of peripheral blood while maintaining cell viability and compatibility with downstream characterization. MAR measurement is a promising approach for directly assaying single-cell therapeutic responses and for identifying cellular subpopulations with phenotypic resistance within heterogeneous tumors.

No abstract

Each cell type in a solid tissue has a characteristic transcriptome and spatial arrangement, both of which are observable using modern spatial omics assays. However, the common practice is still to ignore spatial information when clustering cells to identify cell types. In fact, spatial location is typically considered only when solving the related, but distinct, problem of demarcating tissue domains (which could include multiple cell types). We present BANKSY, an algorithm that unifies cell type clustering and domain segmentation by constructing a product space of cell and neighbourhood transcriptomes, representing cell state and microenvironment, respectively. BANKSY’s spatial kernel-based feature augmentation strategy improves per-formance and scalability on both tasks when tested on FISH-based and sequencing-based spatial omics data. Uniquely, BANKSY identified hitherto undetected niche-dependent cell states in two mouse brain regions. Lastly, we show that quality control of spatial omics data can be formulated as a domain identification problem and solved using BANKSY. BANKSY represents a biologically motivated, scalable, and versatile framework for analyzing spatial omics data.

Abstract-Fast and accurate estimation of the Fourier transform in polar coordinates has long been a major challenge for many image processing applications including phase-correlation based motion estimation. To address this problem, it has been proposed to calculate the Fourier transform coefficients on the pseudo-polar coordinates. To acquire better image registration accuracy, we introduce the generalized pseudo-polar motion estimation framework that encompasses the classic pseudo-polar and related techniques. Experimental results on controlled data as well as real world spectral domain ophthalmic optical coherence tomography images of adults and neonatal patients attest to the effectiveness of the presented method.

The version of this article initially published did not include the probe design software in the Code Availability statement. The statement has been updated to note the availability of probe design software at https://github.com/khchenLab/split-fish. In addition, as a result of a publisher error, the source data for Fig. 1 were provided as .xls files. These have been corrected to .tif files. The errors have been corrected in the HTML and PDF versions of the article.

High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present FISHnCHIPs for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging ~2-35 co-expressed genes that are spatially co-localized in tissues, resulting in similar spatial information as single-gene FISH, but at ~2-20-fold higher sensitivity. Using FISHnCHIPs, we imaged up to 53 gene modules from the mouse kidney and mouse brain, and demonstrated high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPS also revealed spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables robust and scalable spatial transcriptomics analysis of tissues with normal physiology or undergoing pathogenesis.

In the version of Supplementary Table 3 originally posted for this article, an extra G nucleotide was inserted in error into all of the bridge sequences. The error has been corrected online.