Documentation

iCLOTS_demo_edited_2.mp4
Short demonstration video of iCLOTS use with our fluorescence microscopy adhesion application, additional demonstration videos available here.

General information about iCLOTS usage

iCLOTS software generates quantitative metrics from microscopy data obtained during use of a wide range of microfluidic and static assays.

Each application produces detailed output files.

 

All iCLOTS applications follow a common, easy-to-use interactive format.


Users should consider practical experimental design concerns before use.





Users may always access the application-specific documentation available here using the "Help" button in the bottom left of the analysis window.

Downloading and opening iCLOTS in Mac OS

Installation guide



Downloading and opening iCLOTS in Windows OS

Windows operating system



Adhesion image processing application 1: brightfield microscopy

Application that analyzes static, brightfield images of cells (tested extensively on platelets, RBCs, and WBCs) adhered to some surface. May also be suitable for use with preliminary digital pathology approaches, e.g. with blood smears. This application does not separate cells by type, but you could use the post-processing machine learning clustering algorithm to group cells.

Input files:

Parameters to interactively adjust:

Output metrics:

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Some tips from the iCLOTS team:


Learn more about the methods forming the basis of our brightfield microscopy adhesion application:


Adhesion image processing application 2: fluorescence microscopy

Application that analyzes static, fluorescence microscopy images of cells (tested extensively on platelets, RBCs, and WBCs) adhered to some surface. This application does not separate cells by type, but you could use the post-processing machine learning clustering algorithm to group cells.

Input files:

Parameters to interactively adjust:

Output metrics:

Output files:


Some tips from the iCLOTS team:

Learn more about the methods forming the basis of our fluorescence microscopy adhesion application:

Adhesion image processing application 3: filopodia counter

iCLOTS includes a specialized version of the fluorescence microscopy application designed to count and characterize filopodia at single-cell resolution. The Lam lab has found that it can be hard to objectively count filopodia. iCLOTS applies the same parameters (how distinct a filopodia must be, minimum distance from other leading edges) to an image or series of images to reduce this objectivity.

Number of filopodia per cell and descriptive statistics describing filopodia length per cell (minimum, mean, maximum, standard deviation) are reported in addition to cell area and membrane texture.

Input files:

Parameters to interactively adjust:

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Some tips from the iCLOTS team:


Learn more about the methods forming the basis of our filopodia counting microscopy adhesion application:

Adhesion image processing application 4: transient adhesion

iCLOTS includes a specialized version of our adhesion applications coupled with our single cell tracking applications (see below) designed to measure adhesion time of individual cells within a suspension flowing through some kind of channel or microfluidic device, including traditional flow chambers and commercially available devices like the ibidi µSlide. Adhesion time is reported as transit time, the total time the individual cell is present within the field of view.

This application tracks one or many cells within a frame using adapted Crocker and Grier particle tracking methods. Cells are linked into individual trajectories. Cells can travel in any direction(s).Typically this application would be used to track cells transiting a microfluidic device, but other uses may be possible. This application will work for both brightfield and fluorescence microscopy applications, but no fluorescence intensity data is provided in this release.

Input files:

Parameters to interactively adjust:

Output metrics:

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Some tips from the iCLOTS team:


Learn more about the methods forming the basis of our single cell tracking application:

Single cell tracking image processing application 1: brightfield microscopy

This application tracks one or many cells within a frame using adapted Crocker and Grier particle tracking methods. Cells are linked into individual trajectories. Cells can travel in any direction(s). iCLOTS provides a distance traveled, transit time, and velocity (distance/time) for each tracked cell. Typically this application would be used to track cells transiting a microfluidic device, but other uses may be possible. A specialized secondary application analyzes fluorescence microscopy videos of cells transiting any device (see below).

The iCLOTS manuscript demonstrates use of this application primarily with the use of the Lam lab "biophysical flow cytometer" microfluidic device, a research-developed microfluidic designed to provide a relative measure of cell deformability, a mechanical property. A specialized version of the single cell tracking application, for both brightfield and fluorescence microscopy, is also provided (see below.)


Input files:

Parameters to interactively adjust:

Output metrics:

Output files:


Some tips from the iCLOTS team:


Learn more about the methods forming the basis of our single cell tracking application:

Single cell tracking image processing application 2: fluorescence microscopy

This application works in the same way as our single cell tracking image processing application for brightfield microscopy videos, but with an added fluorescence cell intensity output metric to describe the summed intensity of individual cells within a fluorescence microscopy video.

All inputs/outputs, methods, and tips and tricks remain the same. Ideally, the stain used for cells describes some functionality or property of the cell. The developers have found that it can be challenging to detect a strong fluorescence signal from moving cells. If troubleshooting experimental variables such as stain concentration, pump speed, and device height do not result in a stronger signal, use the "Edit contrast" video processing application with a gain (alpha) value that increases signal. Then, divide the output cell intensity metrics by this alpha value to remove any bias.

Users may optionally choose a region of interest to analyze. The application builds a map of potential channels from all fluorescence signal in the video. Usually this is a suitable representation of the microfluidic device.

Specialized single cell tracking image processing applications: deformability assay

The iCLOTS manuscript demonstrates use of single cell tracking capabilities (see above) primarily with use of the Lam lab "biophysical flow cytometer" microfluidic device, a research-developed microfluidic designed to provide a relative measure of cell deformability, a mechanical property. We have included a specialized version of the single cell tracking application, available for both brightfield and fluorescence microscopy, for specific use with this assay. This specialized application tracks cells in the x-direction only and imposes special data quality requirements. This application may be useful for any single-cell resolution channel flow assays. For use with channel flow, rotate the video using our suite of video editing tools so that flow is horizontal.

Input files:


Parameters to interactively adjust:

Output metrics:

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Some tips from the iCLOTS team:



Learn more about the methods forming the basis of our deformability application:


Manuscripts detailing the use of the biophysical flow cytometer device:

Velocity profile image processing application

This application tracks detected features (typically patterns of cells) and their displacement using adapted Shi-Tomasi corner detection and Kanade-Lucas-Tomasi feature tracking methods. This application is ideal for suspensions of cells where cells are significantly overlapping. Cell suspensions can travel in any direction(s). iCLOTS provides velocity (displacement/time) for each tracked feature. Minimum, mean, and maximum velocity values within a frame are reported for each frame. A velocity profile is generated based on a user-specified bin number from all events. Typically this application would be used to track cells transiting a microfluidic device, but other uses may be possible.

This video has been extensively tested on brightfield videomicroscopy. Usage with fluorescently labeled features is likely also possible.

Input files:

Parameters to interactively adjust:

Output metrics:

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Some tips from the iCLOTS team:



Learn more about the methods forming the basis of our velocity profile application:

Microfluidic accumulation image processing application 1: region of interest

Our accumulation-based image processing applications are designed to be multi-scale to fit a variety of researcher's needs. This scale is used to analyze accumulation and occlusion as indicated by fluorescence microscopy signal in any square region of interest selected by the user. The iCLOTS manuscript demonstrates use of this application with a small region from a commercially-available ibidi device.

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Some tips from the iCLOTS team:

Learn more about the methods forming the basis of our multiscale microfluidic accumulation applications:

Microfluidic image processing application 2: complex geometry microfluidic device

Our accumulation-based image processing applications are designed to be multi-scale to fit a variety of researcher's needs. This scale is used to analyze accumulation, occlusion, and obstruction as indicated by fluorescence microscopy signal in a microfluidic device with complex geometry. Only the region of device indicated by a channel stain or the summed signal from a time course is quantified. The iCLOTS manuscript demonstrates use of this application with a branching microfluidic device.

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Input parameters:


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Some tips from the iCLOTS team:


Learn more about the methods forming the basis of our multiscale microfluidic accumulation applications:


Learn more about endothelialized microfluidic devices:

Microfluidic accumulation image processing application 3: microchannel(s)

Our accumulation-based image processing applications are designed to be multi-scale to fit a variety of researcher's needs. This scale is used to analyze accumulation, occlusion, and obstruction as indicated by fluorescence microscopy signal in a series of straight microchannel(s) within some larger device. Individual channels as indicated by a channel stain or left-right extension of the summed signal from a frame are quantified. The iCLOTS manuscript demonstrates use of this application with a set of 32 of the smallest channels within a branching microfluidic device. A single image or a time series of images can be analyzed. Signal from red, blue, and/or green channels can be quantified.


Input files:

Input parameters:


Output files:


Some tips from the iCLOTS team:


Learn more about the methods forming the basis of our multiscale microfluidic accumulation applications:


Learn more about endothelialized microfluidic devices:

Machine learning-enabled post-processing clustering algorithm application

iCLOTS may generate large datasets, depending on file inputs. Should users need additional interpretation of these large datasets, our machine learning application mathematically characterizes natural groupings within any number of pooled datasets, proving useful for detecting cell sample subpopulations or healthy/clinical sample differences. Methods to apply k-means clustering are provided.

This machine learning application applies clustering algorithms to any properly-formatted data, including iCLOTS data. Typically, a series of data points (e.g., cells) are represented by multiple metrics (e.g. velocity, size, or fluorescence intensity). Clustering is an unsupervised machine learning technique designed to mathematically characterize natural groupings within datasets (e.g., cell subpopulations from a single dataset or healthy-clinical dichotomies).

The iCLOTS development team suggests the review paper "A guide to machine learning for biologists" (Greener, Nature Reviews Molecular Cell Biology, 2021) for a better understanding of machine learning. Please also see this documentation's guidance on reporting computational results.

Machine learning workflow (steps), briefly:


Learn more about the methods forming the basis of our machine learning application:

Suite of video and image editing tools

iCLOTS provides a suite of video and image file editing tools to help users format their data for iCLOTS analysis. Briefly, users select a single file or a folder of .png, .jpg, .tif, and/or .avi files for modification. Users may need to perform some operation or edit indicated parameters, some image processing step is applied, and all edited files are returned in a new directory within the original folder. 


Choose a region of interest (ROI)


Crop a video to a specified frame range


Edit the contrast of files


Convert an image sequence to a video


Normalize range of pixel intensity values


Resize file(s)


Rotate file(s)


Convert a video to an image sequence

Tips for reporting computational results

Journals have increasingly high standards for resulting computational results of any kind, including image analysis and machine learning analysis.

Reporting image analysis results

When reporting image analysis results, we suggest performing three specialized analyses:

Reporting machine learning results

This is not an exhaustive resource for interpreting and reporting machine learning results. Your journal likely has more specific guidelines. For more information, please also see:

Software information

Software availability and source code


iCLOTS is a Python-based software built upon many successful open source packages, including:


Data availability

The iCLOTS development team thanks the authors of these projects.  Descriptions of each application reference what  Python libraries were used to implement image processing and machine learning algorithms. Please consider citing these resources as well when publishing iCLOTS-generated data.

The iCLOTS development team would also like to acknowledge several open-source software products that served as an inspiration and guide during the creation of this project. Depending on your analysis goals, you might find these pieces of software more suitable for your own analysis:

Still need help?

Contact us using our form or send an email to iCLOTSsoftware@gmail.com