XPCS – Lightsources.org

XPCS as a powerful tool for nanoparticles analysis in complex biological media

2024/11/212024/12/09

An article published by CNPEM researchers was featured on the Nano Letters scientific journal’s cover and explores how the X-ray Photon Correlation Spectroscopy (XPCS) technique can distinguish protein corona formation from nanoparticle aggregation in complex biological media.

The innovative work, carried out at Sirius, expands analysis capacity in nanomedicine and highlights the XPCS potential to characterize nanoparticle interactions in biological environments in real time, providing a valuable resource for nanobiotechnology research and new biomedical materials development.

The innovative nanoparticles applications in biomedicine

Nanoparticles are tiny structures, with dimensions generally between 1 and 100 nanometers. Due to its size, they can interact with cells, proteins and molecules in a highly precise way, which allows driven delivery of medicines and therapeutic agents. This allows, for example, for cancer treatments to be more effective, by releasing drugs directly into tumor cells, minimizing side effects on healthy tissues.

Furthermore, nanoparticles can be designed for responding to specific stimuli, such as pH, temperature or biological signs, allowing a controlled release of medicines only when necessary.

In the diagnosis area, nanoparticles offer new ways to prematurely detect diseases. They can be linked to specific biomarkers that bind to molecular targets, making it easier to identify cancerous cells or the presence of viruses and bacteria, for example.

The interaction between nanoparticles and proteins in biological systems

These applications, however, are conditioned to a predictable behavior of these nanoparticles in complex biological systems. In some cases, by coming into contact with biological fluids, such as blood, a protein coating can be formed around nanoparticles, a phenomenon known in biomedicine by the English term “protein corona”.

This happens because nanoparticles attract proteins present in the biological environment, forming a “corona” or “crown” around its surface. The formation of this protein corona strongly influences how do nanoparticles interact with cells and tissues in the organism, which can affect its efficacy and safety in medical applications, such as drug therapies, diagnostics, and vaccine development.

For these reasons, studying the protein corona formation and characteristics is crucial for the development of nanoparticles that are safe and effective for biomedical use.

Limitations of optical techniques for analyzing these samples

Optical techniques, such as Fluorescence Correlation Spectroscopy (FCS) and Dynamic Light Scattering (DLS), face significant limitations when analyzing nanoparticles in complex biological environments. One of the main limitations is the need for diluted and transparent samples, which makes it difficult to analyze nanoparticles in highly concentrated media, such as blood and body fluids. In complex media, particles and biomolecules can interfere with light propagation, causing spreading and excessive absorption, which compromises the accuracy of nanoparticle size and concentration measurements.

Furthermore, optical techniques rely on nanoparticle specific properties, which limits its application to particles that present these specific characteristics. For example, in the FCS case, it is necessary that nanoparticles show fluorescence in order to be detected, restricting the technique’s use to fluorescent materials. This is one of the limitations that makes optical techniques less suitable to characterize nanoparticles under realistic conditions and in real time, as in unprocessed samples of biological fluids.

XPCS: A powerful technique for nanoparticles analysis in complex media

The X-ray Photon Correlation Spectroscopy (XPCS) technique appears as a good alternative by offering significant advantages for nanoparticle analysis in complex biological environments, overcoming many of the optical techniques limitations. One of its main advantages is the ability to analyze highly concentrated and complex samples, such as blood and other bodily fluids, without need for dilution or transparency.

New artificial intelligence method to create material ‘fingerprints’

2024/07/162024/10/02

Like people, materials evolve over time. They also behave differently when they are stressed and relaxed. Scientists looking to measure the dynamics of how materials change have developed a new technique that leverages X-ray photon correlation spectroscopy (XPCS), artificial intelligence (AI) and machine learning.

This technique creates “fingerprints” of different materials that can be read and analyzed by a neural network to yield new information that scientists previously could not access. A neural network is a computer model that makes decisions in a manner similar to the human brain.

In a new study by researchers in the Advanced Photon Source (APS) and Center for Nanoscale Materials (CNM) at the U.S. Department of Energy’s (DOE) Argonne National Laboratory, scientists have paired XPCS with an unsupervised machine learning algorithm, a form of neural network that requires no expert training. The algorithm teaches itself to recognize patterns hidden within arrangements of X-rays scattered by a colloid — a group of particles suspended in solution. The APS and CNM are DOE Office of Science user facilities.

“The goal of the AI is just to treat the scattering patterns as regular images or pictures and digest them to figure out what are the repeating patterns. The AI is a pattern recognition expert.” — James (Jay) Horwath, Argonne National Laboratory

“The way we understand how materials move and change over time is by collecting X-ray scattering data,” said Argonne postdoctoral researcher James (Jay) Horwath, the first author of the study.

These patterns are too complicated for scientists to detect without the aid of AI. “As we’re shining the X-ray beam, the patterns are so diverse and so complicated that it becomes difficult even for experts to understand what any of them mean,” Horwath said.

For researchers to better understand what they are studying, they have to condense all the data into fingerprints that carry only the most essential information about the sample. “You can think of it like having the material’s genome, it has all the information necessary to reconstruct the entire picture,” Horwath said.

The project is called Artificial Intelligence for Non-Equilibrium Relaxation Dynamics, or AI-NERD. The fingerprints are created by using a technique called an autoencoder. An autoencoder is a type of neural network that transforms the original image data into the fingerprint — called a latent representation by scientists — and that also includes a decoder algorithm used to go from the latent representation back to the full image.

The goal of the researchers was to try to create a map of the material’s fingerprints, clustering together fingerprints with similar characteristics into neighborhoods. By looking holistically at the features of the various fingerprint neighborhoods on the map, the researchers were able to better understand how the materials were structured and how they evolved over time as they were stressed and relaxed.

AI, simply put, has good general pattern recognition capabilities, making it able to efficiently categorize the different X-ray images and sort them into the map. “The goal of the AI is just to treat the scattering patterns as regular images or pictures and digest them to figure out what are the repeating patterns,” Horwath said. “The AI is a pattern recognition expert.”

Using AI to understand scattering data will be especially important as the upgraded APS comes online. The improved facility will generate 500 times brighter X-ray beams than the original APS. “The data we get from the upgraded APS will need the power of AI to sort through it,” Horwath said.

Read more on Argonne website

Image: The AI-NERD model learns to produce a unique fingerprint for each sample of XPCS data. Mapping fingerprints from a large experimental dataset enables the identification of trends and repeating patterns which aids our understanding of how materials evolve.

Credit: Argonne National Laboratory.

17 meter long detector chamber delivered to CoSAXS

2019/05/142019/05/15

The experimental techniques used at the CoSAXS beamline will use a huge vacuum vessel with possibilities to accommodate two in-vacuum detectors in the SAXS/WAXS geometry.

A major milestone was reached for the CoSAXS project when this vessel was recently delivered, installed and tested.
The main method that will be used at the CoSAXS beamline is called Small Angle X-ray Scattering (SAXS). By detecting the scattered rays coming from the sample at shallow angles, less than 4° typically, it is possible to learn about the size, shape, and orientation of the small building blocks that make up different samples and how this structure gives these materials their properties. The materials to be studied can come from various sources and in diverse states, for example, plastics from packaging, food and how it is processed or proteins in solution which can be used as drugs.
The “co” in CoSAXS stands for coherence, a quality of the synchrotron light optimized at the MAX IV machine, that loosely could be translated as laser-likeness. In the specific case of X-ray Photon Correlation Spectroscopy (XPCS), it lets the researchers not only measure the structure of the building blocks in the sample but also their dynamics – how they change in time.