Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes

Abstract

The architecture of normal and diseased tissues strongly influences the development and progression of disease as well as responsiveness and resistance to therapy. We describe a tissue-based cyclic immunofluorescence (t-CyCIF) method for highly multiplexed immuno-fluorescence imaging of formalin-fixed, paraffin-embedded (FFPE) specimens mounted on glass slides, the most widely used specimens for histopathological diagnosis of cancer and other diseases. t-CyCIF generates up to 60-plex images using an iterative process (a cycle) in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high-dimensional representation. t-CyCIF requires no specialized instruments or reagents and is compatible with super-resolution imaging; we demonstrate its application to quantifying signal transduction cascades, tumor antigens and immune markers in diverse tissues and tumors. The simplicity and adaptability of t-CyCIF makes it an effective method for pre-clinical and clinical research and a natural complement to single-cell genomics.

Multidimensional profiling of drug‐treated cells by Imaging Mass Cytometry

“…In the field of fluorescent cellular imaging microscopy, biomarker analysis in single cells is limited to 4-6 due to the overlapping spectra of fluorescent dyes [13]. Repetitive rounds of staining using the same biological sample are required to achieve multiplexing [14]. New imaging technologies are needed to significantly increase multiplicity in a single sample preparation experiment and perform replicate analyses [15].…”

From single cells to tissue self‐organization

“…When applied to multicellular 3D structures, it will be necessary to contextualize multiple subcellular readouts with tissue organization. Other alternatives to increase number of readouts in imaging have been developed based on cyclic rounds of antibody staining with chemical inactivation of fluorescence [111,112] or more recently with sequential antibody elution and stripping, a method called iterative indirect immunofluorescence imaging (4i) [113]. The latest allows multiplexing of up to 40 fluorescent molecular readouts inside every single cell in fixed samples with complete sample preservation and unprecedented high spatial resolution.…”

Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images

“…However, a major limitation of this approach is that the spatial distribution of cells is lost. In situ methods such as isotope-based imaging (Giesen et al 2014;Angelo et al 2014) or the various multiplexed fluorescence imaging methods (Gerdes et al 2013;Lin et al 2018;Goltsev et al 2018) retain spatial information but require cell identification after data collection. This, in turn, depends on robust methods to reliably identify cell objects.…”