MINFLUX data postprocessing

MINFLUX data postprocessing protocol

Author: Dr. Charlotte Kaplan

             Super-resolution microscopy specialist

             CellNetworks Core Technology Platform

             Heidelberg University, Bioquant

             Im Neuenheimer Feld 267

             Heidelberg, Germany

             Contact: charlotte.kaplan@bioquant.uni-heidelberg.de

Introduction:

MINFLUX data analysis procedures and open source tools have develop extensively since

the publication of the first commercial MINFLUX systems (Schmidt et al., 2021, Nature

Communications).

The community is steadily growing that provides open source data analysis software:

Examples are pyMINFLUX (https://pyminflux.ethz.ch/) , SMAP

(https://github.com/jries/SMAP), and PYME (https://github.com/csoeller/PYME-extra).

Imspector is the software that controls the MINFLUX microscope. The raw data accessibility

and data format has changed with the release of a major software upgrade of Imspector for

MINFLUX in 2025. Imspector version 16.3.15645-m2205 was used to generate the data for

the publication Gao et al., 2025 in Nature Communications. The python scripts published along

with the manuscript of Gao et al., 2025 can process numpy raw data files that are generated by this Imspector

version. When MINFLUX measurements are performed with the newer Imspector version

16.321317-m2410 the Jupyter script in this protocol that converts the numpy raw data file into a csv file cannot

longer be applied. The raw data format that is generated by exporting the data from

Imspector was changed by the manufacturer of the MINFLUX system Abberior Instruments

Gmbh. Please contact the manufacturer for more information regarding the raw data structure

format associated with Imspector version 16.321317-m2410.

Procedure: 

1. Export data from Imspector software version 16.3.15645-m2205

 - Open your MINFLUX .msr file in Imspector

 - In the MINFLUX data panel select the file that you want to export

 - Select the folder path and export the raw data file as a numpy python structured array

2. Install a python environment with the following dependencies:

- python version 3.11.13

- pandas version 2.3.2

- numpy version 1.24.3

- matplotlib version 3.10.6

- seaborn version 0.13.2

- glob version 3.11

- os version 3.12

- fpdf version 1.7.2

3. Set up workspace

- place script: RDtoDF_LocPrec_PDF_dirloop_v4_3.ipynb

into the folder that contains subfolder/s with MINFLUX raw data .npy file

- pip install fpdf (not mandatory)

4. Open the Jupyter script : RDtoDF_LocPrec_PDF_dirloop_v4_3.ipynb as a jupyter

notebook or in jupyter lab via the Anaconda interface or in the set up environment

from a terminal

When running RDtoDF_LocPrec_PDF_dirloop_v4_3 it creates in the subfolders of

your working folder :

a. csv file with the raw data of the last MINFLUX iteration that is used for data

visualization and further analysis

b. csv file with the localization precision and plots

c. plot of MINFLUX quality parameter efo and cfr, dcr if two color acquisition was

performed

d. plot of drift

e. plot of localizations

f. pdf file that provides comments to measurement and some parameters for quality

check

5. Jupyter notebook: Filtered_dataprocessing_v1.ipynb was used in the subfolders to

filter the data as described in Materials and Methods.

The script created a folder with files used for further 

data analysis in the Chimera software.