High-throughput sectioning and optical imaging of tissue samples using traditional immunohistochemical techniques can be costly and inaccessible in resource-limited areas. We demonstrate three-dimensional (3D) imaging and phenotyping in optically transparent tissue using lens-free holographic on-chip microscopy as a low-cost, simple, and high-throughput alternative to conventional approaches. The tissue sample is passively cleared using a simplified CLARITY method and stained using 3,3′-diaminobenzidine to target cells of interest, enabling bright-field optical imaging and 3D sectioning of thick samples. The lens-free computational microscope uses pixel super-resolution and multi-height phase recovery algorithms to digitally refocus throughout the cleared tissue and obtain a 3D stack of complex-valued images of the sample, containing both phase and amplitude information. We optimized the tissue-clearing and imaging system by finding the optimal illumination wavelength, tissue thickness, sample preparation parameters, and the number of heights of the lens-free image acquisition and implemented a sparsity-based denoising algorithm to maximize the imaging volume and minimize the amount of the acquired data while also preserving the contrast-to-noise ratio of the reconstructed images. As a proof of concept, we achieved 3D imaging of neurons in a 200-μm-thick cleared mouse brain tissue over a wide field of view of 20.5 mm 2 . The lens-free microscope also achieved more than an order-of-magnitude reduction in raw data compared to a conventional scanning optical microscope imaging the same sample volume. Being low cost, simple, high-throughput, and data-efficient, we believe that this CLARITY-enabled computational tissue imaging technique could find numerous applications in biomedical diagnosis and research in low-resource settings.

INTRODUCTION

Chronic diseases such as cancer are increasing in the developing world (1, 2). To accurately diagnose and treat these diseases, tissue biopsies are necessary and widely considered among the gold standard techniques. However, in many developing countries, limited access to the infrastructure needed to process tissue biopsies can create delays and inaccuracies in diagnosis and thus increase the incidence of more advanced disease (3). The current gold standard diagnostic methods (for example, tissue biopsy and histopathological analysis) require tissue preparation facilities and complex procedures. The high cost of equipment and the lack of trained health care professionals may result in delays in diagnosis. Although technological advances have allowed for physicians to remotely access medical data to perform diagnosis, there still remains an urgent need for reliable and inexpensive means for disease identification, particularly in low-resource settings, for pathology, biomedical research, and related applications.

The CLARITY technique offers a unique method for whole-tissue biopsies to be labeled and imaged without the need for serial sectioning (4–6). This technique preserves spatial relationships and tissue microstructures by forming a tissue-hydrogel hybrid with increased permeability for deep optical interrogation, which can be useful for three-dimensional (3D) mapping and identification of rare markers (4, 7). The simplified CLARITY method (SCM) (8) creates optically transparent tissue with proteins and nucleic acids intact using an easy-to-follow protocol without the need for any additional equipment. On the basis of the passive CLARITY technique (9), SCM forms a hydrogel matrix throughout the tissue with the polymerization of acrylamide to maintain tissue integrity (8, 9). The light scattering due to endogenous lipids within the tissue is then passively eliminated using an inexpensive detergent solution, and the intact tissue can be specifically labeled for multiple markers of interest.

Fluorescent microscopy has been typically used to image CLARITY samples, because most tissue-clearing methods have been developed for labeling using fluorescent probes (10–13). However, many laboratories in resource-limited settings have little or no access to high-cost microscopy equipment and filter sets required for fluorescence imaging. Colorimetric staining (8) serves as an alternative and cost-effective method of specific labeling that is compatible with bright-field 3D imaging modalities, also enabling the use of portable imaging devices. In addition, it allows for the specimen to be imaged repeatedly without any degradation in quality, whereas photobleaching and signal fading may occur in a fluorescently stained sample (14).

Lens-free holographic on-chip microscopy can be an ideal complementary technique to enable colorimetric phenotyping of cleared tissue using SCM in low-resource settings (15–27). Eliminating lenses and other complex optical components, lens-free microscopy takes advantage of state-of-the-art consumer-grade image sensors with small pixel size and large pixel counts to offer phase and amplitude imaging over a wide field of view (FOV) (for example, >20 mm2) and depth of field (for example, >0.5 mm) with submicron resolution, and the capability to computationally provide 3D sectioning of samples using a compact and cost-effective setup. As an alternative to lens-free imaging, 3D holographic microscopy of samples using lens-based designs has also been demonstrated, although over a smaller imaging volume due to the limited FOV of microscope objective lenses (28–33). Our lens-free on-chip microscope design (see Fig. 1A) works in the bright-field transmission mode, where the sample is illuminated by a partially coherent light source, with a large illumination aperture that is placed 5 to 10 cm above the sample (z 1 distance), and the holographic shadow of the sample is captured by the image sensor chip positioned in close proximity to the sample (z 2 distance < 1 to 2 mm). The light that is scattered by the sample volume interferes with the unscattered light, forming the sample’s in-line hologram (34–40). For highly scattering and/or absorbing samples such as uncleared tissue, the sample needs to be sectioned into thin slices, typically under 10 μm, to be successfully imaged in the transmission mode (24–26). If the sample is too thick, the photons that are transmitted will likely have undergone multiple random scattering events along their path, causing loss of useful information and a reduction in signal-to-noise ratio. The CLARITY process greatly reduces this unwanted scattering and absorption by removing most lipids while maintaining proteins and nucleic acids intact, thus increasing the effective imaging depth (that is, the sample thickness that can be reconstructed/imaged).

Fig. 1 Lens-free on-chip microscopy setup and image processing steps. (A) Schematic of the lens-free on-chip imaging setup. The cleared tissue is loaded in a polydimethylsiloxane (PDMS)/glass chamber filled with a refractive index matching solution. A sealant is applied on the sides to avoid evaporation and leakage. RIMS, refractive index matching solution; CMOS, complementary metal-oxide semiconductor. (B) Lens-free image processing workflow is outlined.

Here, we demonstrate lens-free 3D imaging of SCM-prepared tissue samples stained with 3,3′-diaminobenzidine (DAB), which reduces the cost and complexity associated with traditional CLARITY techniques, while also introducing portability. To maximize the volume of the imaged tissue by our on-chip microscope and minimize the amount of acquired data while also preserving a good contrast-to-noise ratio (CNR) in our images, we optimized the illumination wavelength, tissue thickness, pH of the staining solution, and the number of heights used in lens-free on-chip image acquisition. Furthermore, we exploited the natural sparsity of the holographically reconstructed images to effectively remove image artifacts resulting from random scattering of the background and the interference from out-of-focus cells. On the basis of these optimizations, we achieved 3D holographic imaging of a 200-μm-thick section of mouse brain tissue over an FOV of ~20.5 mm2 using parvalbumin, a common protein found in a subset of neurons, as the target marker. We also confirmed that the holographically reconstructed sections of the brain tissue agree well with the images acquired using a high-end scanning optical microscope. Moreover, to image the same 3D tissue volume, the lens-free microscope acquires ~21 times fewer number of raw images (corresponding to ~11 times less image data) and can be over two orders of magnitude faster compared to a conventional scanning optical microscope that images the same sample volume. With the advantages of being low cost, simple, high-throughput, and data-efficient, this SCM-enabled computational tissue imaging method can potentially be used for disease diagnosis and biomedical research in resource-limited environments.