Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining protocols face several challenges, such as a need for extensive, manual processing of samples, substantial time delays, altered tissue homeostasis, limited choice of contrast agents, 2D imaging instead of 3D tomography and many more. Label-free optical technologies, on the other hand, do not rely on exogenous and artificial markers, by exploiting intrinsic optical contrast mechanisms, where the specificity is typically less obvious to the human observer. Over the past few years, digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. In this review article, we provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.

This paper experimentally examines different configurations of a multi-camera array microscope (MCAM) imaging technology. The MCAM is based upon a densely packed array of “micro-cameras” to jointly image across a large field-of-view (FOV) at high resolution. Each micro-camera within the array images a unique area of a sample of interest, and then all acquired data with 54 micro-cameras are digitally combined into composite frames, whose total pixel counts significantly exceed the pixel counts of standard microscope systems. We present results from three unique MCAM configurations for different use cases. First, we demonstrate a configuration that simultaneously images and estimates the 3D object depth across a 100×135mm² FOV at approximately 20 µm resolution, which results in 0.15 gigapixels (GP) per snapshot. Second, we demonstrate an MCAM configuration that records video across a continuous 83×123mm² FOV with twofold increased resolution (0.48 GP per frame). Finally, we report a third high-resolution configuration (2 µm resolution) that can rapidly produce 9.8 GP composites of large histopathology specimens.

Immune cell activity is a major factor for disease progression in inflammatory bowel diseases (IBD). Classifying the type and functional state of immune cells is therefore crucial in clinical diagnostics of IBD. Label-free optical technologies exploiting NADH and FAD autofluorescence, such as multiphoton microscopy, have been used to describe tissue morphology in healthy and inflamed colon samples. Nevertheless, a strategy for the identification of single immune cell subtypes within the tissue is yet to be developed. This work aims to initiate an understanding of autofluorescence changes depending on immune cell type and activation state. For this, NADH and FAD autofluorescence signals of different murine immune cell subtypes under native conditions, as well as upon in vitro stimulation and cell death, have been evaluated. Autofluorescence was assessed using flow cytometry and multiphoton microscopy. Our results reveal significantly increased NADH and FAD signals in innate immune cells compared to adaptive immune cells. This allowed identification of relative amounts of neutrophils and CD4+ T cells in mixed cell suspensions, by using NADH signals as a differentiation marker. Furthermore, in vitro stimulation significantly increased NADH and FAD autofluorescence in adaptive immune cells and macrophages. Cell death induced a significant drop in NADH autofluorescence, while FAD signals were hardly affected. Taken together, these results demonstrate the value of autofluorescence as a tool to characterize immune cells in different functional states, paving the way to the label-free clinical classification of IBD in the future.

Group 3 innate lymphoid cells (ILC3s) are crucial mediators of immunity and epithelial barrier function during immune responses against extracellular bacteria. Here, we identify Interferon regulatory factor 1 (IRF-1), a transcription factor previously associated with type 1 immunity, as an essential regulator of intestinal ILC3 accumulation and effector cytokine production. We demonstrate that IRF-1 is upregulated in the context of infection with the enteropathogen Citrobacter rodentium and that its presence is central for anatomical containment and prevention of pathogen dissemination. We furthermore show that IRF-1 is required in order for intestinal ILC3s to produce large amounts of the protective effector cytokine IL-22 early in the course of infection. On a molecular level, our data indicate that IRF-1 controls ILC3 numbers and their activation by direct transcriptional regulation of the IL-12Rβ1 chain, thereby allowing ILCs to physiologically respond to IL-23 stimulation.

Quantitative phase imaging (QPI) is a well-known label-free microscopy approach that can detect phase delay introduced by semitransparent cells and tissue.¹ Due in part to its ability to provide quantitative information about primarily transparent biological specimens with low phototoxicity, QPI has become an invaluable tool in scientific and clinical studies, including for monitoring neuronal firing² and cancer cell line detection,³ to name a few. Besides scalar phase contrast, transparent specimens also exhibit alternative and important endogenous optical contrast mechanisms, including anisotropic properties such as material birefringence and orientation. Indeed, the orientation of molecular arrangements in lipid membranes can now be quantitatively monitored with polarization-sensitive microscopes for studying multi-organelle interactive activities.^4,5 There are naturally a variety of polarization-sensitive microscope arrangements, including early analog designs,^6,7 differential interference contrast (DIC) methods,⁸ and contemporary digital approaches that reconstruct quantitative specimen retardance and orientation.^{9 – 11} These methods have been applied to study small model organisms^12,13 and to assist clinical diagnosis.^{14 – 17}