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  • Researchers open door to stain-free labeling of cellular components

    11.11.22 - Scientists at EPFL and the Consiglio Nazionale delle Ricerche (CNR), the University Federico II, and CEINGE-Biotecnologie avanzate in Naples, Italy, have developed a new method to screen individual cells quickly and reliably without fluorescence labeling. Their work, published in the journal Nature Photonics, opens new avenues in early tumor diagnosis and drug development. Under the microscope, healthy and unhealthy cells can be very difficult to distinguish. Scientists use stains or fluorescent tags targeting specific proteins to identify cell types, characterize their state, and study the impact of drugs and other therapies. While its impact on medicine has been transformational, the approach has its limitations. For one, tagging cells is expensive, time-consuming, and strongly dependent on the researcher’s skill. On top of that, the staining process can be detrimental to the cells under investigation. That’s why researchers have been developing alternative ways to quickly and reliably screen individual cells. In a recent article published in the journal Nature Photonics, researchers from EPFL’s School of Engineering and colleagues from the Institute of Applied Sciences & Intelligent Systems, CNR, in Pozzuoli, Italy, present a stain-free approach capable of accurately distinguishing specific regions within living cells. Uniquely combining holographic imaging and microfluidics with neural network-based signal processing, the work paves the way for liquid biopsies for circulating tumor cell detection and high-throughput assays for drug testing. Optics Lab postdoc Carlo Gigli with lab head Demetri Psaltis © Alain Herzog From the phase delay to the refractive index The study builds on learning tomography, a method previously developed by Demetri Psaltis and his team at the EPFL Optics Laboratory. Rather than using a microscope to create a visual image of the specimen under study, learning tomography relies on quantitative phase imaging, a holographic imaging approach that reveals the phase delay incurred as the microscope’s light beam passes through the matter that makes up the cell. Repeating this process at several different angles and running the phase data through a neural network allowed the researchers to generate 3D maps of the refractive index of each individual voxel – each three-dimensional volume resolved by the method. “The refractive index is influenced by the density of molecules and the material,” explains Psaltis. Increasing the number of iterations further improved the accuracy of the refractive index distribution estimate. The new method for three dimensional imaging of cells without fluorescence staining © Alain Herzog Classifying cellular components In their publication, Psaltis and his team present how they overcame a long-standing limitation of quantitative phase imaging approaches: the inability to identify intracellular components. “Using a self-clustering approach that groups voxels with a similar refractive index coupled with machine learning tools allowed us to assemble the clusters into shapes that we could classify. Different types of nuclei, for example, have different indices of refraction,” says Psaltis. Closing this gap paves the way for quantitative phase imaging to deliver insights previously only obtainable using fluorescence microscopy. A second challenge was developing a method to screen cells that did not require immobilizing them. The solution to this challenge came from co-author Pietro Ferraro and his laboratory at CNR, who had vast experience working on in-flow tomography using lab-on-chip devices. “The idea was to put the cells in a fluidic channel 50 to 100 microns across and let the flow velocity gradient in the channel rotate the cells,” says Psaltis. “By observing the cells as they tumble along the channel using a stationary beam and detector, we can detect the phase delay, estimate the orientation of the cell, and apply our learning tomography approach to generate the 3D refractive index maps.” “The achievable transverse resolution is of half a micron to one micron,” says Psaltis. “We can’t detect individual proteins, but we can see protein aggregates, which tend to be tens of microns across. It also let us assess the size of the nucleus and the outline of the cell, which becomes less smooth when cells become cancerous.” The researchers validated their methodology by comparing their findings with observations made using confocal fluorescence microscopy, today’s gold standard in 3D cellular imaging. The new method for three dimensional imaging of cells without fluorescence staining © Alain Herzog © Alain Herzog High-throughput screening of individual cells An essential application of stain-free cell screening is liquid biopsies that allow the detection of circulating cancer cells, used both to identify cancer types in surgery and as an early diagnostic tool for cancer metastasis. Another is drug development. Many diseases, such as Parkinson’s, are associated with cross-linked proteins. The approach developed by Psaltis and his collaborators offers a highly efficient, non-invasive way to evaluate the effectiveness of drugs designed to break down these cross-linked proteins in real-time by repeatedly running treated cells through the imaging setup. In the same way, the approach could be used to give researchers new insights into the real-time effects of pathogens on healthy cells. According to Psaltis, future work will involve applying machine learning tools to extract biologically relevant information and concrete diagnoses from the estimated refractive index distribution. Jan Overney

EPFL news News feed from imaging

  • Researchers open door to stain-free labeling of cellular components

    11.11.22 - Scientists at EPFL and the Consiglio Nazionale delle Ricerche (CNR), the University Federico II, and CEINGE-Biotecnologie avanzate in Naples, Italy, have developed a new method to screen individual cells quickly and reliably without […]

  • A new device for early diagnosis of degenerative eye disorders

    28.10.22 - Researchers at an EPFL lab have developed an ophthalmological device that can be used to diagnose some degenerative eye disorders long before the onset of the first symptoms. In early clinical trials, the prototype was shown to produce […]

  • Call for imaging projects: interdisciplinarity in the spotlight!

    26.10.22 - Four ambitious projects were selected among very strong submissions to the second “EPFL Call for Interdisciplinary Projects in Imaging”. These grants were awarded to projects connecting at least two different Schools, with each […]

  • “We have to be forward-looking”

    21.10.22 - Touradj Ebrahimi, a professor at EPFL, has been selected for this year’s Society of Motion Picture & Television Engineers (SMPTE) Progress Medal – the most coveted award in the field of image processing. We spoke with this […]

  • 800kCHF raised for “open imaging” projects at EPFL

    20.10.22 - The EPFL Center for Imaging raised 800kCHF to foster projects that aim at accelerating the diffusion of imaging technology to non-expert users. The EPFL Center for Imaging successfully raised more than 800kCHF in 2022 for four projects […]

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Imaging Grants.

To encourage cross-fertilization between various disciplines and closer interactions between EPFL actors in imaging, we have launched a “Call for Interdisciplinary Projects in Imaging”, a series of grants to support collaborative projects aimed at advancing imaging technology at EPFL.

Description

When it comes to generating 3D digital geometric models of historical buildings, the automation of methods is still limited. Existing research focused on sacral structures, on 3D model generation of the exterior envelope of buildings and on segmentation of interior spaces. The goal of this project is (i) to develop a data acquisition and post-processing… Continue reading 3D imaging of historical buildings


Status: Ongoing
Description

A key tool for studying the dynamics of living systems is the light microscope. Microscopes allow real-time recording of spontaneous or evoked spatio-temporal dynamics, data that can be used to develop models for how complex systems function. Today, cutting-edge microscopes can image below the diffraction limit of light (super-resolution microscopy), or over days, gently enough… Continue reading Spatiotemporal adaptive microscope control, driven by biological events


Status: Ongoing
Description

Many questions in biology, from development to neuroscience and medicine require the identification of finegrained behaviors. We will develop novel computer vision and natural language processing technology to improve behavioral analysis in biology and medicine. Specifically, we will build deep learning models that can efficiently learn joint representations from video and heterogeneous data sources (e.g.,… Continue reading Video-based action segmentation by learning world models from language


Status: Ongoing
Description

Next-generation radio telescopes such as the Square Kilometer Array (SKA) will observe the sky with unprecedented resolution, sensitivity, and survey speed. However, this precise instrument will demand reliable, precise, and high dynamic range deconvolution techniques to form images. The popular CLEAN algorithm, while efficient, often produces images of suboptimal quality. In recent years convex and… Continue reading Learned Scalable high Dynamic Range imaging in radio astronomy


Status:
Description

Scanning probe methods – and in particular, the combination of scanning ion conductance microscopy (SICM) and scanning electrochemical microscopy (SECM) – have emerged as unique tools for studying materials and mechanisms in complex, multistep chemical reactions such as CO2 reduction. However, they are notoriously slow in image acquisition, making them ill-suited for studying the dynamics of energy conversion processes. In this project, these two EPFL labs will develop advanced hardware and software components for a unique, fast SICM-SECM imaging method that can be easily deployed within the EPFL community, and beyond. Their method has great potential for the design of energy devices, as well as emerging cross-disciplinary applications such as the nanoelectrochemistry of single-cell signaling.


Status: Ongoing
Description

3D image reconstruction or depth estimation is at the core of applications in navigation as well as Earth system science. Significant advances have been made in the field of computer vision to obtain 3D information from various types of cameras. Yet, these techniques still face limitations for a number of applications. In this project, the… Continue reading A more effective 3D-imaging system for Earth System Science and Navigation


Status: Ongoing
Description

Each human cell contains around two meters of DNA tightly packaged in its nucleus. An exquisite organization is critical to ensure that the DNA can be accessed by the many important genetic processes. This organization is achieved by wrapping the DNA around millions of tiny protein spindles, forming a complex called chromatin. Chromatin governs many key cellular functions and, when malfunctions in its organization can lead to serious diseases.


Status: Ongoing
Description

In this project, scientists from two EPFL labs will combine their know-how to develop a new high-speed microscopy system that can reveal single-molecule dynamics with unprecedented detail, including in liquids. The system will also allow scientists to assess how individual molecules behave, interact and self-organize at the solid-liquid interface. More specifically, they will enhance the… Continue reading High-speed multimodal super-resolution microscopy


Status: Ongoing
Description

When it comes to characterizing mechanics at the cellular scale, the accuracy and precision of current methods are still limited. In this project, Prof. Kolinski and Prof. Persat will connect imaging to mechanical measurements by developing a set of hardware and software tools that can measure microscale 3D force fields and surface stresses.


Status: Ongoing
Description

Spatial transcriptomics – a nascent field arising from the combination of cutting-edge microscopy with gene-specific in-situ labeling – can be used to generate large gene expression profiles of messenger RNA. This gives scientists an indication of the relative expression rates of different genes in the same environment. EPFL scientists at these two labs are are… Continue reading Towards more accurate large-scale gene expression profiles


Status: Ongoing

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