Automated 3D scoring of fluorescence in situ hybridization (FISH) using a confocal whole slide imaging scanner

Taking molecular pathology to the next level: Whole slide multicolor confocal imaging with the Pannoramic Confocal digital pathology scanner

The emergence and fast advance of digital pathology allows the acquisition, digital storage, interactive recall and analysis of morphology at the tissue level. When applying immunohistochemistry, it also affords the correlation of morphology with the expression of one or two specific molecule of interest. The rise of fluorescence pathology scanners expands the number of detected molecules based on multiplex labeling. The Pannoramic Confocal (created by 3DHistech, Hungary) is a first-of-the-kind digital pathology scanner that affords not only multiplexed fluorescent detection on top of conventional transmission imaging, but also confocality. We have benchmarked this scanner in terms of stability, precision, light efficiency, linearity and sensitivity. X-Y stability and relocalisation precision were well below resolution limit (≤50 nm). Light throughput in confocal mode was 4-5 times higher than that of a point scanning confocal microscope, yielding similar calculated confocal intensities but with the potential for improving signal to noise ratio or scan speed. Response was linear with R²  ≥ 0.9996. Calibrated measurements showed that using indirect labeling ≥2000 molecules per cell could be well detected and imaged on the cell surface. Both standard-based and statistical post-acquisition flatfield corrections are implemented. We have also measured the point spread function (PSF) of the instrument. The dimensions of the PSF are somewhat larger and less symmetric than of the theoretical PSF of a conventional CLSM, however, the spatial homogeneity of these parameters allows for obtaining a specific system PSF for each optical path and using it for optional on-the-fly deconvolution. In conclusion, the Pannoramic Confocal provides sensitive, quantitative widefield and confocal detection of multiplexed fluorescence signals, with optical sectioning and 3D reconstruction, in addition to brightfield transmission imaging. High speed scanning of large samples, analysis of tissue heterogeneity, and detection of rare events open up new ways for quantitatively analyzing tissue sections, organoid cultures or large numbers of adherent cells.

Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing

Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.