De-scattering with excitation patterning in temporally-focused microscopy (DEEP- TFM)

Point-scanning two-photon microscopy is used routinely for in vivo, volumetric biological imaging, especially in deep tissues. Despite the excellent penetration depth, a conventional point-scanning two-photon microscopy is slow due to the need for raster scanning and imaging time scales linearly with increasing volume, hampering studies of fast biological dynamics. An attractive alternative to point-scanning geometries is wide-field two-photon microscopy, typically called temporal focusing microscopy (TFM) since optical sectioning is achieved by focusing a beam temporally while maintaining wide-field illumination. However, TFM suffers from scattering in tissue resulting in limited imaging depth. Please click Additional Files below to see the full abstract

Compressed full-field Fourier transform spectrometry

Imaging Fourier transform spectrometry (IFTS) can be used for hyperspectral imaging in the wide-field mode. Wide-field hyperspectral imaging is a powerful technique for quantifying functional and morphological states of cells and tissues. Multiplexed fluoresce imaging, Multicolor spectral karyotyping of human chromosomes, spectral fluorescence resonance energy transfer(sp-FRET) and spontaneous Raman imaging are few examples. Unlike other hyperspectral imaging modalities, IFTS measures the Fourier transform of the spectrum of light at each pixel in the wide-field image, and traditionally, the inverse Fourier transform is used to extract the spectral information. The spectral recovery process (for each pixel) can be captured by a set of liner equations written in the matrix form below. Please click Additional Files below to see the full abstract

Three-dimensional image cytometer based on widefield structured light microscopy and high-speed remote depth scanning

A high throughput 3D image cytometer have been developed that improves imaging speed by an order of magnitude over current technologies. This imaging speed improvement was realized by combining several key components. First, a depth-resolved image can be rapidly generated using a structured light reconstruction algorithm that requires only two wide field images, one with uniform illumination and the other with structured illumination. Second, depth scanning is implemented using the high speed remote depth scanning. Finally, the large field of view, high NA objective lens and the high pixelation, high frame rate sCMOS camera enable high resolution, high sensitivity imaging of a large cell population. This system can image at 800 cell/sec in 3D at submicron resolution corresponding to imaging 1 million cells in 20 min. The statistical accuracy of this instrument is verified by quantitatively measuring rare cell populations with ratio ranging from 1:1 to 1:10[superscript 5].National Institutes of Health (U.S.) (Grant 9P41EB015871-26A1)National Institutes of Health (U.S.) (Grant 5R01EY017656-02)National Institutes of Health (U.S.) (Grant 5R01 NS051320)National Institutes of Health (U.S.) (Grant 4R44EB012415-02)National Science Foundation (U.S.) (Grant CBET-0939511)Singapore-MIT Alliance for Research and TechnologyMIT Skoltech InitiativeHamamatsu CorporationDavid H. Koch Institute for Integrative Cancer Research at MIT (Bridge Project Initiative

From Hours to Seconds: Towards 100x Faster Quantitative Phase Imaging via Differentiable Microscopy

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the speed of electronic hardware. Complementarily, to improve throughput further, here we propose to acquire images in a compressed form such that more information can be transferred beyond the existing electronic hardware bottleneck. To this end, we present a learnable optical compression-decompression framework that learns content-specific features. The proposed differentiable quantitative phase microscopy ($\partial \mu$) first uses learnable optical feature extractors as image compressors. The intensity representation produced by these networks is then captured by the imaging sensor. Finally, a reconstruction network running on electronic hardware decompresses the QPM images. In numerical experiments, the proposed system achieves compression of $\times$ 64 while maintaining the SSIM of $\sim 0.90$ and PSNR of $\sim 30$ dB on cells. The results demonstrated by our experiments open up a new pathway for achieving end-to-end optimized (i.e., optics and electronic) compact QPM systems that may provide unprecedented throughput improvements

Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology

Deep neural network models can learn clinically relevant features from millions of histopathology images. However generating high-quality annotations to train such models for each hospital, each cancer type, and each diagnostic task is prohibitively laborious. On the other hand, terabytes of training data -- while lacking reliable annotations -- are readily available in the public domain in some cases. In this work, we explore how these large datasets can be consciously utilized to pre-train deep networks to encode informative representations. We then fine-tune our pre-trained models on a fraction of annotated training data to perform specific downstream tasks. We show that our approach can reach the state-of-the-art (SOTA) for patch-level classification with only 1-10% randomly selected annotations compared to other SOTA approaches. Moreover, we propose an uncertainty-aware loss function, to quantify the model confidence during inference. Quantified uncertainty helps experts select the best instances to label for further training. Our uncertainty-aware labeling reaches the SOTA with significantly fewer annotations compared to random labeling. Last, we demonstrate how our pre-trained encoders can surpass current SOTA for whole-slide image classification with weak supervision. Our work lays the foundation for data and task-agnostic pre-trained deep networks with quantified uncertainty.Comment: 18 pages, 8 figure

Highly sensitive quantitative phase microscopy and deep learning aided with whole genome sequencing for rapid detection of infection and antimicrobial resistance

Current state-of-the-art infection and antimicrobial resistance (AMR) diagnostics are based on culture-based methods with a detection time of 48–96 h. Therefore, it is essential to develop novel methods that can do real-time diagnoses. Here, we demonstrate that the complimentary use of label-free optical assay with whole-genome sequencing (WGS) can enable rapid diagnosis of infection and AMR. Our assay is based on microscopy methods exploiting label-free, highly sensitive quantitative phase microscopy (QPM) followed by deep convolutional neural networks-based classification. The workflow was benchmarked on 21 clinical isolates from four WHO priority pathogens that were antibiotic susceptibility tested, and their AMR profile was determined by WGS. The proposed optical assay was in good agreement with the WGS characterization. Accurate classification based on the gram staining (100% recall for gram-negative and 83.4% for gram-positive), species (98.6%), and resistant/susceptible type (96.4%), as well as at the individual strain level (100% sensitivity in predicting 19 out of the 21 strains, with an overall accuracy of 95.45%). The results from this initial proof-of-concept study demonstrate the potential of the QPM assay as a rapid and first-stage tool for species, strain-level classification, and the presence or absence of AMR, which WGS can follow up for confirmation. Overall, a combined workflow with QPM and WGS complemented with deep learning data analyses could, in the future, be transformative for detecting and identifying pathogens and characterization of the AMR profile and antibiotic susceptibility

Rosa26-GFP Direct Repeat (RaDR-GFP) Mice Reveal Tissue- and Age-Dependence of Homologous Recombination in Mammals In Vivo

Homologous recombination (HR) is critical for the repair of double strand breaks and broken replication forks. Although HR is mostly error free, inherent or environmental conditions that either suppress or induce HR cause genomic instability. Despite its importance in carcinogenesis, due to limitations in our ability to detect HR in vivo, little is known about HR in mammalian tissues. Here, we describe a mouse model in which a direct repeat HR substrate is targeted to the ubiquitously expressed Rosa26 locus. In the Rosa26 Direct Repeat-GFP (RaDR-GFP) mice, HR between two truncated EGFP expression cassettes can yield a fluorescent signal. In-house image analysis software provides a rapid method for quantifying recombination events within intact tissues, and the frequency of recombinant cells can be evaluated by flow cytometry. A comparison among 11 tissues shows that the frequency of recombinant cells varies by more than two orders of magnitude among tissues, wherein HR in the brain is the lowest. Additionally, de novo recombination events accumulate with age in the colon, showing that this mouse model can be used to study the impact of chronic exposures on genomic stability. Exposure to N-methyl-N-nitrosourea, an alkylating agent similar to the cancer chemotherapeutic temozolomide, shows that the colon, liver and pancreas are susceptible to DNA damage-induced HR. Finally, histological analysis of the underlying cell types reveals that pancreatic acinar cells and liver hepatocytes undergo HR and also that HR can be specifically detected in colonic somatic stem cells. Taken together, the RaDR-GFP mouse model provides new understanding of how tissue and age impact susceptibility to HR, and enables future studies of genetic, environmental and physiological factors that modulate HR in mammals.National Institutes of Health (U.S.) (Program Project Grant P01-CA026731)National Institutes of Health (U.S.) (R33-CA112151)National Institute of Environmental Health Sciences (P30-ES002109)Singapore-MIT Alliance for Research and Technology CenterNational Institutes of Health (U.S.) (P41-EB015871)National Cancer Institute (U.S.) (P30-CA014051