Spectral Unmixing Methods and Tools for the Detection and Quantitation of Collagen and Other Macromolecules in Tissue Specimens

Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists

Our understanding of the biology and management of human disease has undergone a remarkable evolution in recent decades. Improved understanding of the roles of complex immune populations in the tumor microenvironment has advanced our knowledge of antitumor immunity, and immunotherapy has radically improved outcomes for many advanced cancers. Digital pathology has unlocked new possibilities for the assessment and discovery of the tumor microenvironment, such as quantitative and spatial image analysis. Despite these advances, tissue-based evaluations for diagnosis and prognosis continue to rely on traditional practices, such as hematoxylin and eosin staining, supplemented by the assessment of single biomarkers largely using chromogenic immunohistochemistry (IHC). Such approaches are poorly suited to complex quantitative analyses and the simultaneous evaluation of multiple biomarkers. Thus, multiplex staining techniques have significant potential to improve diagnostic practice and immuno-oncology research. The different approaches to achieve multiplexed IHC and immunofluorescence are described in this study. Alternatives to multiplex immunofluorescence/IHC include epitope-based tissue mass spectrometry and digital spatial profiling (DSP), which require specialized platforms not available to most clinical laboratories. Virtual multiplexing, which involves digitally coregistering singleplex IHC stains performed on serial sections, is another alternative to multiplex staining. Regardless of the approach, analysis of multiplexed stains sequentially or simultaneously will benefit from standardized protocols and digital pathology workflows. Although this is a complex and rapidly advancing field, multiplex staining is now technically feasible for most clinical laboratories and may soon be leveraged for routine diagnostic use. This review provides an update on the current state of the art for tissue multiplexing, including the capabilities and limitations of different techniques, with an emphasis on potential relevance to clinical diagnostic practice.

Direct fluorescence imaging of lignocellulosic and suberized cell walls in roots and stems

Investigating plant structure is fundamental in botanical science and provides crucial knowledge for the theories of plant evolution, ecophysiology and for the biotechnological practices. Modern plant anatomy often targets the formation, localization and characterization of cellulosic, lignified or suberized cell walls. While classical methods developed in the 1960s are still popular, recent innovations in tissue preparation, fluorescence staining and microscopy equipment offer advantages to the traditional practices for investigation of the complex lignocellulosic walls. Our goal is to enhance the productivity and quality of microscopy work by focusing on quick and cost-effective preparation of thick sections or plant specimen surfaces and efficient use of direct fluorescent stains. We discuss popular histochemical microscopy techniques for visualization of cell walls, such as autofluorescence or staining with calcofluor, Congo red (CR), fluorol yellow (FY) and safranin, and provide detailed descriptions of our own approaches and protocols. Autofluorescence of lignin in combination with CR and FY staining can clearly differentiate between lignified, suberized and unlignified cell walls in root and stem tissues. Glycerol can serve as an effective clearing medium as well as the carrier of FY for staining of suberin and lipids allowing for observation of thick histological preparations. Three-dimensional (3D) imaging of all cell types together with chemical information by wide-field fluorescence or confocal laser scanning microscopy (CLSM) was achieved.

Semi-blind sparse affine spectral unmixing of autofluorescence-contaminated micrographs

MOTIVATION

Spectral unmixing methods attempt to determine the concentrations of different fluorophores present at each pixel location in an image by analyzing a set of measured emission spectra. Unmixing algorithms have shown great promise for applications where samples contain many fluorescent labels; however, existing methods perform poorly when confronted with autofluorescence-contaminated images.

RESULTS

We propose an unmixing algorithm designed to separate fluorophores with overlapping emission spectra from contamination by autofluorescence and background fluorescence. First, we formally define a generalization of the linear mixing model, called the affine mixture model (AMM), that specifically accounts for background fluorescence. Second, we use the AMM to derive an affine nonnegative matrix factorization method for estimating fluorophore endmember spectra from reference images. Lastly, we propose a semi-blind sparse affine spectral unmixing (SSASU) algorithm that uses knowledge of the estimated endmembers to learn the autofluorescence and background fluorescence spectra on a per-image basis. When unmixing real-world spectral images contaminated by autofluorescence, SSASU greatly improved proportion indeterminacy as compared to existing methods for a given relative reconstruction error.

AVAILABILITY AND IMPLEMENTATION

The source code used for this paper was written in Julia and is available with the test data at https://github.com/brossetti/ssasu.