Tag: Gustaf Sjösten

Label-free nanofluidic scattering microscopy of size and mass of single diffusing molecules and nanoparticles published in Nature Methods

Kymographs of DNA inside Channel II. (Image by the Authors.)
Label-free nanofluidic scattering microscopy of size and mass of single diffusing molecules and nanoparticles
Barbora Špačková, Henrik Klein Moberg, Joachim Fritzsche, Johan Tenghamn, Gustaf Sjösten, Hana Šípová-Jungová, David Albinsson, Quentin Lubart, Daniel van Leeuwen, Fredrik Westerlund, Daniel Midtvedt, Elin K. Esbjörner, Mikael Käll, Giovanni Volpe & Christoph Langhammer
Nature Methods 19, 751–758 (2022)
doi: 10.1038/s41592-022-01491-6

Label-free characterization of single biomolecules aims to complement fluorescence microscopy in situations where labeling compromises data interpretation, is technically challenging or even impossible. However, existing methods require the investigated species to bind to a surface to be visible, thereby leaving a large fraction of analytes undetected. Here, we present nanofluidic scattering microscopy (NSM), which overcomes these limitations by enabling label-free, real-time imaging of single biomolecules diffusing inside a nanofluidic channel. NSM facilitates accurate determination of molecular weight from the measured optical contrast and of the hydrodynamic radius from the measured diffusivity, from which information about the conformational state can be inferred. Furthermore, we demonstrate its applicability to the analysis of a complex biofluid, using conditioned cell culture medium containing extracellular vesicles as an example. We foresee the application of NSM to monitor conformational changes, aggregation and interactions of single biomolecules, and to analyze single-cell secretomes.

Gustaf Sjösten defended his Master thesis on 17 May 2021. Congrats!

Gustaf Sjösten defended his Master thesis in MPCAS at the Chalmers University of Technology on 17 May 2021. Congrats!

Artistic rendering of a light source illumination a single biomolecule and scattered photons. (Image by Gustaf Sjösten, inspired by an image created by Barbora Spackova)
Title: Deep Learning for Nanofluidic Scattering Microscopy

A novel technique for label-free, real-time characterization of single biomolecules called Nanofluidic Scatter Microscopy (NSM) has recently been developed by the Langhammer research group at Chalmers. We have created a machine learning (ML) framework consisting of deep convolutional neural networks such as U-nets, ResNets and YOLO in order to characterize single biomolecules through kymographs collected through NSM, as an alternative approach to a standard data analysis method (SA). As a laser irradiates visible light onto single biomolecules freely diffusing in solution inside nanofluidic channels, the biomolecule and the nanochannel scatter light coherently into the collection optics, such that the nanochannels improve the optical contrast of the imaged biomolecules by several orders of magnitude. A video of the total scattering intensity is then recorded with a high frame rate camera (capturing 200 fps) in order to capture the movement of the molecules as well as the optical contrast of the biomolecules with respect to the nanochannel. From the movement of one single biomolecule, it is possible to predict its diffusion constant, which can then be used to infer the hydrodynamic radius of the biomolecule. Additionally, the predicted optical contrast of one single biomolecule can in turn be used to infer its molecular weight. From the combination of hydrodynamic radius and molecular weight, information about the conformal state of single biomolecules can be inferred. In this thesis, we show that the ML approach yields results comparable to the SA which was developed independently of the ML technique for biomolecules in the weight span 66-669 kDa, and we also show that the ML technique is superior to the SA in other regards, such as computational speed and potential to characterize smaller molecules. The results of the data analysis performed with the ML framework will also make an appearance in the first paper on the NSM technique which has been submitted for publication and is currently under review.

​Name of the master programme: MPCAS – Complex Adaptive Systems
Supervisor: Giovanni Volpe, Daniel Midtvedt
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Anton Jansson

Place: Online via Zoom
Time: 17 May, 2021, 16:00

Gustaf Sjösten joins the Soft Matter Lab

Gustaf Sjösten joined the Soft Matter Lab on 1 September 2020.

Gustaf Sjösten is a Master student in the Complex Adaptive Systems Master at Chalmers University of Technology.

He will work on his Master thesis on the characterization of nanoparticles in nanochannels with machine learning methods.