AFM_SMFS

Matlab coding for AFM SMFS raw data post-processing > localization and classification of rupture events

This repository contains Matlab codes to process Atomic Force Microscopy raw single molecule force spectroscopy data. In the present context, cantilevers were functionalized to target hyaluronic acid molecules on live cells. This data processing allows for distinguishing between hyaluronic acid molecules anchored or not anchored to the cell cytoskeleton by looking at the probe/molecule rupture events. Similar methodologies were reported here: Chu C. et al. 2013, Sariisik E. et al. 2015

All codes were tested on Matlab R2016a version

Raw data

The raw data are force-spectroscopy .txt files from AFM experiments. They contain four columns: cantilever height [m], cantilever vertical deflection [N], series time [s], segment time [s]. All the experiments in this context were carried out with a Nanowizard 3 microscope from JPK. The built-in software provide .txt files in this form (comments are preceded by #)

Pre-processed data

Raw data (input) are processed individually with the Matlab code AFM1_contactpoint.m. This code is needed to:

• fit the contact point with the ratio-of-variance method (see Gavara N. 2016),
• ask the user if happy with the contact point fitting,
• correct the drift between extend and retract baselines,
• correct for tip-sample separation. Pre-processed data are obtained as output.

Localise and classify probe/molecule rupture events

Pre-processed data (input) are analyzed with the Matlab code AFM2_findpeaks.m to localize and classify rupture events. This code is needed to:

• localize rupture events by screening the first derivative of the force signal,
• classify rupture events as cytoskeleton-anchored ruptures or membrane tether ruptures. The ouput consists of two matrices called csk (for cytoskeleton-anchored) and tet (for membrane tethers) which contains all the classified rupture events for the pre-processed data.

Detailed code description

AFM1_contactpoint.m

This algorithm takes raw file from AFM microscope (.txt) as input and fit the contact point, correct retract drift and tip-sample separation. New .txt files are saved as output in a folder of choice containing cantilever height, vertical deflection, time and segment.

1. INPUT - information about the performed AFM experiment need to be entered by the user (spring constant of the cantilever used, input folder and Matlab working folder)
2. open input folder and list file names for next step
3. FOR cycle which opens one file at the time from the input folder and perform post-processing steps
   1. open file
   2. save data from file into arrays
   3. fit contact point on extend curve
   4. plot data after fitting the CP for user verification
   5. save pre-processed file as .txt files in the output folder

AFM2_findpeaks.m

This algorithm takes pre-processed data as input and localize/classify rupture events on the retract curve. Two matrices containing the classified events details are returned as output. Cytoskeleton-anchored rupture events are preceded by a rise in force due to the spring-like behavior of the actin cytoskeleton; membrane tether rupture events are preceded by a plateau in force. The slope of the curve prior to rupture is therefore used as a classifier: if "horizontal" the rupture is classified as a membrane tether, if at "higher angles" the rupture is classified as a cytoskeleton-anchored. To define a slope as "horizontal" the baseline of the extend curve of the acquired data can be considered (user input): this holds zero force and therefore its variations in slope should correspond to the one of a plateau.

1. INPUT - information about the performed AFM experiment need to be entered by the user (thresholds for rupture event classification, input folder and Matlab working folder)
2. open input folder and list file names for next step
3. initialize rupture events counter
4. FOR cycle which opens one file at the time from the input folder and perform post-processing steps
   1. open file
   2. save data from file into arrays
   3. find derivative of smoothed data
   4. localize rupture events by thresholding peaks on the derivative
   5. classify peaks as cytoskeleton-anchored or membrane tethers
5. save rupture events in correspondent output matrix (csk or tet); each matrix contains all classified rupture events, in terms of distance from contact point (first column), force at rupture (second column), slope prior to rupture (third column) and data ID (fourth column).