An official website of the United States government
Abstract Chemical imaging, especially mid-infrared spectroscopic microscopy, enables label-free biomedical analyses while achieving expansive molecular sensitivity. However, its slow speed and poor image quality impede widespread adoption. We present a microscope that provides high-throughput recording, low noise, and high spatial resolution where the bottom-up design of its optical train facilitates dual-axis galvo laser scanning of a diffraction-limited focal point over large areas using custom, compound, infinity-corrected refractive objectives. We demonstrate whole-slide, speckle-free imaging in ~3 min per discrete wavelength at 10× magnification (2 μm/pixel) and high-resolution capability with its 20× counterpart (1 μm/pixel), both offering spatial quality at theoretical limits while maintaining high signal-to-noise ratios (>100:1). The data quality enables applications of modern machine learning and capabilities not previously feasible – 3D reconstructions using serial sections, comprehensive assessments of whole model organisms, and histological assessments of disease in time comparable to clinical workflows. Distinct from conventional approaches that focus on morphological investigations or immunostaining techniques, this development makes label-free imaging of minimally processed tissue practical.
more »
« less
Clinical and Biomedical Applications of Lensless Holographic Microscopy
Many clinical procedures and biomedical research workflows rely on microscopy, including diagnosis of cancer, genetic disorders, autoimmune diseases, infections, and quantification of cell culture. Despite its widespread use, traditional image acquisition and review by trained microscopists is often lengthy and expensive, limited to large hospitals or laboratories, precluding use in point‐of‐care settings. In contrast, lensless or lensfree holographic microscopy (LHM) is inexpensive and widely deployable because it can achieve performance comparable to expensive and bulky objective‐based benchtop microscopes while relying on components that cost only a few hundred dollars or less. Lab‐on‐a‐chip integration is practical and enables LHM to be combined with single‐cell isolation, sample mixing, and in‐incubator imaging. Additionally, many manual tasks in conventional microscopy are instead computational in LHM, including image focusing, stitching, and classification. Furthermore, LHM offers a field of view hundreds of times greater than that of conventional microscopy without sacrificing resolution. Here, the basic LHM principles are summarized, as well as recent advances in artificial intelligence integration and enhanced resolution. How LHM is applied to the above clinical and biomedical applications is discussed in detail. Finally, emerging clinical applications, high‐impact areas for future research, and some current challenges facing widespread adoption are identified.
more »
« less
- Award ID(s):
- 2114275
- PAR ID:
- 10537517
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Laser & Photonics Reviews
- ISSN:
- 1863-8880
- Page Range / eLocation ID:
- 2400197
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Chondrocyte viability is a crucial factor in evaluating cartilage health. Most cell viability assays rely on dyes and are not applicable forin vivoor longitudinal studies. We previously demonstrated that two-photon excited autofluorescence and second harmonic generation microscopy provided high-resolution images of cells and collagen structure; those images allowed us to distinguish live from dead chondrocytes by visual assessment or by the normalized autofluorescence ratio. However, both methods require human involvement and have low throughputs. Methods for automated cell-based image processing can improve throughput. Conventional image processing algorithms do not perform well on autofluorescence images acquired by nonlinear microscopes due to low image contrast. In this study, we compared conventional, machine learning, and deep learning methods in chondrocyte segmentation and classification. We demonstrated that deep learning significantly improved the outcome of the chondrocyte segmentation and classification. With appropriate training, the deep learning method can achieve 90% accuracy in chondrocyte viability measurement. The significance of this work is that automated imaging analysis is possible and should not become a major hurdle for the use of nonlinear optical imaging methods in biological or clinical studies.more » « less
-
Recent advances in transcriptomic analysis at single-cell resolution reveal cell-to-cell heterogeneity in a biological sample with unprecedented resolution. Partitioning single cells in individual micro-droplets and harvesting each cell's mRNA molecules for next-generation sequencing has proven to be an effective method for profiling transcriptomes from a large number of cells at high throughput. However, the assays to recover the full transcriptomes are time-consuming in sample preparation and require expensive reagents and sequencing cost. Many biomedical applications, such as pathogen detection, prefer highly sensitive, reliable and low-cost detection of selected genes. Here, we present a droplet-based microfluidic platform that permits seamless on-chip droplet sorting and merging, which enables completing multi-step reaction assays within a short time. By sequentially adding lysis buffers and reactant mixtures to micro-droplet reactors, we developed a novel workflow of single-cell reverse transcription loop-mediated-isothermal amplification (scRT-LAMP) to quantify specific mRNA expression levels in different cell types within one hour. Including single cell encapsulation, sorting, lysing, reactant addition, and quantitative mRNA detection, the fully on-chip workflow provides a rapid, robust, and high-throughput experimental approach for a wide variety of biomedical studies.more » « less
-
Neueder, Andreas (Ed.)Light microscopy methods have continued to advance allowing for unprecedented analysis of various cell types in tissues including the brain. Although the functional state of some cell types such as microglia can be determined by morphometric analysis, techniques to perform robust, quick, and accurate measurements have not kept pace with the amount of imaging data that can now be generated. Most of these image segmentation tools are further burdened by an inability to assess structures in three-dimensions. Despite the rise of machine learning techniques, the nature of some biological structures prevents the training of several current day implementations. Here we present PrestoCell, a novel use of persistence-based clustering to segment cells in light microscopy images, as a customized Python-based tool that leverages the free multidimensional image viewer Napari. In evaluating and comparing PrestoCell to several existing tools, including 3DMorph, Omipose, and Imaris, we demonstrate that PrestoCell produces image segmentations that rival these solutions. In particular, our use of cell nuclei information resulted in the ability to correctly segment individual cells that were interacting with one another to increase accuracy. These benefits are in addition to the simplified graphically based user refinement of cell masks that does not require expensive commercial software licenses. We further demonstrate that PrestoCell can complete image segmentation in large samples from light sheet microscopy, allowing quantitative analysis of these large datasets. As an open-source program that leverages freely available visualization software, with minimum computer requirements, we believe that PrestoCell can significantly increase the ability of users without data or computer science expertise to perform complex image analysis.more » « less
-
ABSTRACT Materials processing and additive manufacturing afford exciting opportunities in biomedical research, including the study of cell-material interactions. However, some of the most efficient materials for microfabrication are not wholly suitable for biological applications, require extensive post-processing or exhibit high mechanical stiffness that limits the range of applications. Conversely, materials exhibiting high cytocompatibility and low stiffness require long processing times with typically decreased spatial resolution of features. Here, we investigated the use of hexanediol diacrylate (HDDA), a classic and efficient polymer for stereolithography, for oligodendrocyte progenitor cell (OPC) culture. We developed composite HDDA-polyethylene glycol acrylate hydrogels that exhibited high biocompatibility, mechanical stiffness in the range of muscle tissue, and high printing efficiency at ∼5 μm resolution.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/lpor.202400197
