Automated Identification and Quantification of Signals in Multichannel Immunofluorescence Images

Abstract

Multimarker fluorescence analysis of tissue specimens offers the opportunity to probe the expression levels and locations of multiple markers in a single sample. Software is needed to fully capitalize on the advantages of this technology for sensitive, quantitative, and multiplexed data collection. A major challenge has been the automated identification and quantification of signals. We report on the software SignalFinder-IF, which meets that need. SignalFinder-IF uses a newly developed algorithm called Segment-Fit Thresholding, which showed robust performance for automated signal identification in side-by-side comparisons with several current methods. Two utilities provided with SignalFinder-IF enable downstream analyses. The first allows the quantification and mapping of relationships between an unlimited number of markers through user-defined sequences of AND, OR, and NOT operators. The second produces composite pictures of the signals or colocalization analysis on brightfield hematoxylin and eosin images, which is useful for understanding the morphologies and locations of cells meeting specific marker criteria. SignalFinder-IF enables high-throughput, rigorous analyses of whole-slide, multimarker data, and it promises to open new possibilities in many research and clinical applications.

Imaging Mass Spectrometry and Lectin Analysis of N-Linked Glycans in Carbohydrate Antigen–Defined Pancreatic Cancer Tissues

“…An automated thresholding program, SignalFinder-IF, was used to identify real signals on fluorescently labeled tissues. The algorithm uses multiround segmentation to separate nontissue and tissue backgrounds and signal levels of fluorescence ( 20 , 24 , 25 ). The immunofluorescence data were overlaid onto MALDI mass and H&E images using the custom-built Overlay program ( 5 ) and Canvas 14.…”

“…Hematoxylin and eosin staining were performed following a standard protocol. and signal levels of fluorescence [20,24,25] . The immunofluorescence data was overlaid onto MALDI mass and H&E images using the custom-built Overlay program (5) and Canvas 14.…”

Glycogenolysis Is Crucial for Astrocytic Glycogen Accumulation and Brain Damage after Reperfusion in Ischemic Stroke

“…The S100b is mainly in astrocytic soma that reports only about 15% of the cell volume (Bushong et al, 2002), and most of the volume that also contains about half of the glycogen (Oe et al, 2016) and presumably related enzymes is actually in the small processes that were not analyzed in the present study. In addition, the expression of phosphorylated enzyme does not equal the actual activity of the enzyme, and the relationship between fluorescence intensity and expression of the target protein is not linear (Barnett et al, 2019;Odell and Cook, 2013), which illustrates that immunofluorescence is imprecise and only a semiquantitative method to evaluate enzyme activities in vivo. More precise methods might be developed to quantify astrocyte-specific enzyme activity in vivo.…”

Tools for generating and analyzing glycan microarray data

“…Using this tool a user can input simply the image obtained from the scanner (.tif file) and the array map file (.gal file) and it will automatically identify and align the spots. To do this, the software uses a segment and fit thresholding algorithm, which is also useful for immunofluorescence images [ 46 – 47 ]. The user then has the ability to override or flag any spots to be ignored in the analysis.…”