From 3adc6ff29c06744b9ae9be52c3199b32530a84c4 Mon Sep 17 00:00:00 2001 From: Holger Hennig Date: Wed, 21 Dec 2016 18:13:18 +0100 Subject: [PATCH] Issues/#101 (#112) * resolves * resolves --- examples/index.html | 26 + imagingflowcytometry/IFC_label_free.html | 581 +++++++++++++++++++++++ imagingflowcytometry/index.html | 208 +++----- 3 files changed, 666 insertions(+), 149 deletions(-) create mode 100644 imagingflowcytometry/IFC_label_free.html diff --git a/examples/index.html b/examples/index.html index f73e3c7..321c39e 100644 --- a/examples/index.html +++ b/examples/index.html @@ -1036,6 +1036,32 @@

Specialized src= "http://d1zymp9ayga15t.cloudfront.net/images/ExampleColocalization_Orig.png"> + + + + +

Pipelines for Imaging Flow Cytometry

+

Imaging flow cytometry combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. CellProfiler can be used to analyze the resulting images from imaging flow cytometry, whether brightfield, darkfield, or fluorescence.
+ [IFC website]
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+ + + + + + + + CellProfiler + + + + + + + + + + + + +
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+ Download +
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+ Help + +
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+ About +
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+ CellProfiler Analyst +
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Imaging flow cytometry analysis using + CellProfiler

+

Note: There is a newer protocol available here.

+

Imaging flow cytometry combines + the high-throughput capabilities of conventional + flow cytometry with single-cell imaging. + CellProfiler can be used to analyze the resulting + images from imaging flow cytometry, whether + brightfield, darkfield, or fluorescence.
+
+ In some cases, even unlabeled cells can be scored + for particular phenotypes. In the workflow outlined + below, we have demonstrated label-free prediction + of DNA contentand quantification of the mitotic + cell cycle phases by applying supervised machine + learning to morphological features extracted from + brightfield and the typically-ignored darkfield + images of cells from an imaging cytometer. This + method facilitates non-destructive monitoring of + cells avoiding potentially confounding effects of + fluorescent stains while maximizing available + fluorescence channels. Although Blasi et al. focuses on a label-free analysis using bright-field and dark-field alone, we emphasize that this protocol can be applied in assays that involve any number of fluorescent channels and is not limited to label-free assays.
+
+ Paper:
+ T. Blasi, H. Hennig, H.D. Summers, F.J. Theis, D. Davies, A. Filby, A.E. Carpenter, P. Rees. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Comm. 7, 10256 (2016). PMID: 26739115 [link to paper at Nature Communications]
+
+ Protocol: + [Download all protocols]
+
+

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Step 1: + Extract single cell images & identify cell + populations with IDEAS + software

+ + [PDF Protocol] + [example input data] + [example output data] +

+
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+ + + + Step 2: Preprocess the + single cell images combine them to montages of + images using Matlab

+ [PDF Protocol] + [example input data] + [source code] + [example output data] +

+
+
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+ + + + Step 3: Segment images + and extract features using + CellProfiler

+ [PDF Protocol] + [example input data] + [example CellProfiler pipeline] + + [example output data] +

+
+
+
+

Future developments

+

A new protocol for analyzing imaging flow + cytometry data in high-throughput is currently + under development:

+

new protocol

+

 

+

In addition, analyzing imaging flow cytometry + data in high-throughput will also become more + streamlined using any image analysis software via + the following protocol (under development):

+

new protocol

+ +
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+
+
+
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+ + diff --git a/imagingflowcytometry/index.html b/imagingflowcytometry/index.html index bb08368..b6219fe 100644 --- a/imagingflowcytometry/index.html +++ b/imagingflowcytometry/index.html @@ -249,7 +249,7 @@ - CellProfiler + CellProfiler ImagingFlowCytometry @@ -377,62 +377,32 @@

Imaging flow cytometry analysis using - CellProfiler

Imaging flow cytometry combines + CellProfiler +

Imaging flow cytometry (IFC) combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. CellProfiler can be used to analyze the resulting images from imaging flow cytometry, whether - brightfield, darkfield, or fluorescence.
-
- In some cases, even unlabeled cells can be scored - for particular phenotypes. In the workflow outlined - below, we have demonstrated label-free prediction - of DNA contentand quantification of the mitotic - cell cycle phases by applying supervised machine - learning to morphological features extracted from - brightfield and the typically-ignored darkfield - images of cells from an imaging cytometer. This - method facilitates non-destructive monitoring of - cells avoiding potentially confounding effects of - fluorescent stains while maximizing available - fluorescence channels.
-
- Paper: Blasi T, Hennig H, Summers - HD, Theis FJ, Cerveira J, Patterson JO, Davies D, - Filby A, Carpenter AE, Rees P (2016). Label-free - cell cycle analysis for high-throughput imaging - flow cytometry. Nat Commun 7:10256 / doi - PMID: 26739115. PMCID: In Press. (Research - article)
-
- Protocol: - [Download all protocols]
-
+ brightfield, darkfield, or fluorescence.

+

We here provide an open-source IFC protocol described in Hennig et al. (2016). This protocol aims to enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye. Compensated data files from an imaging flow cytometer (the proprietary .cif file format) can be read and resulting image tiles are generated. The image tiles are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analyzed using cutting-edge machine learning and clustering approaches using user-friendly platforms such as CellProfiler Analyst or scripting languages such as R or Python.

+

Note: This is a more user-friendly and streamlined protocol as compared to Blasi et al. (2016), however, the former protocol is still available here.
+

+

Paper:
+ H. Hennig, P. Rees, T. Blasi, L. Kamentsky, J. Hung, D. Dao, A.E. Carpenter, and A. Filby. An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Method, in press (2016) [link to paper at Methods]
+ T. Blasi, H. Hennig, H.D. Summers, F.J. Theis, D. Davies, A. Filby, A.E. Carpenter, P. Rees. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Comm. 7, 10256 (2016). PMID: 26739115 [link to paper at Nature Communications]

+

Protocol: [download Protocol_README.txt]
+

+ "width:1000px;height:140px;-webkit-border-radius:30px;-moz-border-radius:30px; border-radius:30px;border:2px solid #A9A9A9;background-color:#FFFFFF;">
-

Preparatory Step: + Identify cell + populations using gating in IDEAS + software. Export population as cif file Step 1: - Extract single cell images & identify cell - populations with IDEAS - software

- - [PDF Protocol] - [example input data] - [example output data] -

+ width="130">

+

@@ -448,26 +418,22 @@

Imaging flow cytometry analysis using "20" src= "http://d1zymp9ayga15t.cloudfront.net/images/Step1pic.jpg" style="vertical-align:top" width="80"> - - Step 2: Preprocess the - single cell images combine them to montages of - images using Matlab

- [PDF Protocol] + Step 1: Automatically generate tiles of 1000 single cell images per tile, using a python app (alternatively a Matlab script is available). The app reads a cif file and writes the tiles (which are tif image files) to the output folder.

+

- [example input data] [example input cif file][python app for tiling] - [source code] [Matlab script for tiling] - [example output data] -


+ "http://cellprofiler-org.s3.amazonaws.com/ifc/Step1_output_tiff_montages.zip">[example output data]

+ +

@@ -483,104 +449,48 @@

Imaging flow cytometry analysis using "20" src= "http://d1zymp9ayga15t.cloudfront.net/images/Step2pic2.jpg" style="vertical-align:top" width="100"> - - Step 3: Segment images - and extract features using - CellProfiler

+ Step 2: Segment images + and extract features in + CellProfiler. The example CellProfiler pipeline exports the features as csv files. The pipeline also generates a CellProfiler Analyst properties file for the machine learning in step 3.

+ + "http://d1zymp9ayga15t.cloudfront.net/Protocol/Step3Protocol.pdf" target="_blank"> [PDF Protocol] + "http://cellprofiler-org.s3.amazonaws.com/ifc/Step2_input_tiled_tifs.zip"> [example input data] - [example CellProfiler pipeline] + "http://cellprofiler-org.s3.amazonaws.com/ifc/Step2_CellProfiler.cpproj"> + [CellProfiler pipeline] - [example output data] -


+ "http://cellprofiler-org.s3.amazonaws.com/ifc/Step2_CP_output.zip"> + [example output data]
-
-


+

- - Step 4 (optional): - Machine Learning for label-free prediction of - cell phenotypes

+ + Step 3: Use any programming language for supervized or unsupervized machine learning,such as python or R. A user-friendly option for machine learning is the softwareCellProfiler Analyst. For this, load the properties file in CellProfilerAnalyst.

+ - Preprocessing: - [PDF protocol] - [example input data] - [source code] - [example output data file]
+ "http://cellprofiler.org/cp-analyst/" target="_blank">[CellProfiler Analyst website]
- Classification: - [PDF protocol] - [example input file] - [source code] - [example output data file]
- Regression: - - [PDF protocol] - [example input data file] - - [source code] - [example output data file] -

+ "space"> +


-

Future developments

-

A new protocol for analyzing imaging flow - cytometry data in high-throughput is currently - under development:

-

new protocol

-

 

-

In addition, analyzing imaging flow cytometry - data in high-throughput will also become more - streamlined using any image analysis software via - the following protocol (under development):

-

new protocol

- +