Skip to main content

FACS tools for split fluorescent proteins

Project description

altFACS

Author: David Brown

Last Edited: 2020-August-07

Aim:

This package is intended to standardise and simplify the investigation of protein-protein interactions by flow cytometry.

Explanation:

In the Huang Lab we are interested in developing new tools to examine biological interactions. Split fluorescent proteins have proven to be a useful tool for the tagging and study of endogenous proteins in living cells, and we have been trying to maximise their utility. Appropriate use of a split fluorescent protein as a probe requires a good understanding of the complementation process, whereby the two halves of the split meet and fold to form the mature fluorescent protein. Complementation can be studied biochemically, however we can exploit the self-reporting nature of fluorescent proteins to study complementation in vivo by microscopy or or flow cytometry which offers a higher throughput.

Flow cytometry and fluorescence activated cell sorting (FACS) are more frequently used to distinguish cell populations based on a characteristic intensity profile. In our case we often use it to study how proteins behave at a range of concentrations. This alternative approach to FACS is the main purpose of the altFACS package.

Example Plots:

Example altFACS plots

Example altFACS Plots:

  • A. Raw flow cytometry events.
  • B. Scatter gating. Events from A after events saturating in any channel have been removed from all channels. Events likely to correspond to live cells have been gated based on a contour map.
  • C. Singlet gating. Events from B after likely to contain more than one cell (below the line) are excluded.
  • D. Negative control without transfection. Events from C after fluorescence gates have been set to contain 99% of the population.
  • E. Transfected with CloGFP(1-10) only.
  • F. Positive control with full length sfGFP::CTLA:T2A:mTagBFP
  • G. Fitting of full length GFP in BFP+ cells. altFACS facilitates model fitting to flow cytometry data.
  • H. CloGFP with wild type GFP11::CTLA:T2A:mTagBFP.
  • I. CloGFP with Y9F mutant GFP11::CTLA:T2A:mTagBFP.
  • J-L. Data from panels G-I rescaled for comparison.

We conclude that split CloGFP complementation efficiency is much less than 100%, and that the Y9F mutation in the GFP11 fragment has no impact of split CloGFP complementation efficiency.

Installation

Currently, altFACS is available on GitHub or on the test.Pypi site. Most requirements will install automatically, but you may need to install fcsparser before installing altFACS.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

altFACS-1.0.8.tar.gz (23.9 kB view details)

Uploaded Source

Built Distribution

altFACS-1.0.8-py3-none-any.whl (31.0 kB view details)

Uploaded Python 3

File details

Details for the file altFACS-1.0.8.tar.gz.

File metadata

  • Download URL: altFACS-1.0.8.tar.gz
  • Upload date:
  • Size: 23.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.3

File hashes

Hashes for altFACS-1.0.8.tar.gz
Algorithm Hash digest
SHA256 70567c601f22456c1545d82803c5633b670eb49152b249e7397ea0c1576328fc
MD5 37b90206fe7f6c374ec2fcca78440221
BLAKE2b-256 6698c72ca7bec97258a46cd9c3fd6e37292207239d7114a222db6a7d255b03fe

See more details on using hashes here.

File details

Details for the file altFACS-1.0.8-py3-none-any.whl.

File metadata

  • Download URL: altFACS-1.0.8-py3-none-any.whl
  • Upload date:
  • Size: 31.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.3

File hashes

Hashes for altFACS-1.0.8-py3-none-any.whl
Algorithm Hash digest
SHA256 59d28dec3a565710109e343fe5e54713a43bcd3891d3c1d192be728129560398
MD5 6ab2ce1d16a2377d007a6f3d8114f5ff
BLAKE2b-256 443e6476abb631f5d246e1e07c3410addc837557dfe388dbb36696e7510ebde7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page