New methods for clearing and expansion of biological objects create large, transparent samples that can be rapidly imaged using lightsheet microscopy. Resulting image acquisitions are terabytes in size and consist of many large, unaligned image tiles that suffer from optical distortions. We developed the BigStitcher software that efficiently handles and reconstructs large multi-tile, multi-view acquisitions compensating all major optical effects, thereby making single-cell resolved whole-organ datasets amenable to biological studies.Sample clearing [chung, Hama] and expansion microscopy (ExM) [exp] are powerful protocols that create large, transparent volumes of whole tissues and organisms. Using lightsheet microscopy, these samples can be imaged with subcellular resolution in their entirety within a few hours [Tomer]. These acquisitions have the potential to be powerful tools for whole-tissue and whole-organism studies since they preserve endogenous fluorescent proteins and are compatible with most staining methods (Supplementary Fig. 1).However, raw data acquired by the microscope is not directly suitable for visualization and analysis. Many large, overlapping three-dimensional (3d) image tiles are collected that amount to many terabytes in size and require image alignment ( Fig. 1d-m). Due to sample-induced scattering of the lightsheet in the direction of illumination [scat], 3d image tiles are typically acquired twice while alternating illumination from opposing directions to achieve full coverage ( Fig. 1d and Supplementary Fig. 2). Similarly, emitted light is distorted by the sample, effectively limiting maximal imaging depth at which useful data can be collected (Fig. 1n). Additionally, sample-induced light refractions cause depth-and wavelength-dependent aberrations in the acquired images (Fig. 1j,k). To reconstruct these complex datasets and make the data transparently accessible we developed the BigStitcher software. It enables interactive visualization using BigDataViewer [bdv], fast and precise alignment, real-time fusion, deconvolution, as well as support for alignment of multitile acquisition taken from different physical orientations, so called multi-tile views, thereby effectively doubling the size of specimens that can be imaged (Fig. 1n), and in the case of orthogonal views rendering the resolution isotropic.

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New methods for clearing and expansion of biological objects create large, transparent samples that can be rapidly imaged using light-sheet microscopy. Resulting image acquisitions are terabytes in size and consist of many large, unaligned image tiles that suffer from optical distortions. We developed the BigStitcher software that efficiently handles and reconstructs large multi-tile, multi-view acquisitions compensating all major optical effects, thereby making single-cell resolved whole-organ datasets amenable to biological studies.Sample clearing [1,2] and expansion microscopy (ExM) [3] are powerful protocols that create large, transparent volumes of whole tissues and organisms. Using light-sheet microscopy [4][5][6], these samples can be imaged with subcellular resolution in their entirety within a few hours [7]. These acquisitions have the potential to be powerful tools for whole-tissue and whole-organism studies since they preserve endogenous fluorescent proteins and are compatible with most staining methods (Supplementary Fig. 1).However, raw data acquired by the microscope is not directly suitable for visualization and analysis. Many large, overlapping three-dimensional (3d) image tiles are collected that amount to many terabytes in size and require image alignment ( Fig. 1d-n). Due to sample-induced scattering of the light-sheet in the direction of illumination [8], 3d image tiles are typically acquired twice while alternating illumination from opposing directions to achieve full coverage ( Fig. 1d and Supplementary Fig. 2). Similarly, emitted light is distorted by the sample, effectively limiting maximal imaging depth at which useful data can be collected (Fig. 1n). Additionally, sample-induced light refractions cause depth-and wavelength-dependent aberrations in the acquired images (Fig. 1j,k). To reconstruct and make these complex datasets easily accessible we developed the BigStitcher software. It enables interactive visualization using BigDataViewer [9], fast and precise alignment, real-time fusion, deconvolution, as well as support for alignment of multi-tile acquisition taken from different physical orientations, so-called multi-tile views, thereby effectively doubling the size of specimens that can be imaged (Fig. 1n), while further orthogonal views can render the resolution isotropic.Microscopy acquisitions are saved in a multitude of vendor-specific and custom formats. We developed an extendable, user-friendly interface that automatically imports almost any format and extracts relevant metadata such as illumination directions, sample rotation, and approximate image positions using Bioformats [10] (Supplementary Note 1). Alternatively, the importer supports interactive placement of image tiles using regular grids or simple text file-based definitions (Supplementary Fig. 3). BigStitcher accesses image data through memory-cached, virtual loading [11], optionally combined with virtual flatfield correction ( Supplementary Fig. 4 and Supplementary Note 2). Performance is optimal when images are stor...

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Patient-derived 3D cell culture systems are currently advancing cancer research since they potentiate the molecular analysis of tissue-like properties and drug response under well-defined conditions. However, our understanding of the relationship between the heterogeneity of morphological phenotypes and the underlying transcriptome is still limited. To address this issue, we here introduce “pheno-seq” to directly link visual features of 3D cell culture systems with profiling their transcriptome. As prototypic applications breast and colorectal cancer (CRC) spheroids were analyzed by pheno-seq. We identified characteristic gene expression signatures of epithelial-to-mesenchymal transition that are associated with invasive growth behavior of clonal breast cancer spheroids. Furthermore, we linked long-term proliferative capacity in a patient-derived model of CRC to a lowly abundant PROX1-positive cancer stem cell subtype. We anticipate that the ability to integrate transcriptome analysis and morphological patho-phenotypes of cancer cells will provide novel insight on the molecular origins of intratumor heterogeneity.