Nature Releases Seven Noteworthy Technologies in 2024

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Nature Releases Seven Noteworthy Technologies in 2024

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Nature Releases Seven Noteworthy Technologies in 2024

On January 22, 2024, Nature published a list of seven noteworthy technologies for the year. The prominent advancements include large DNA segment insertion, artificial intelligence-designed proteins, brain-machine interfaces, cell atlases, super-resolution microscopy, 3D printing of nanomaterials, and DeepFake detection.

This year, a significant shift was observed as artificial intelligence (AI) emerged as a central pillar supporting many exciting technological innovations.

Large DNA Segment Insertion

In December 2023, the U.S. FDA approved the first CRISPR-Cas9-based gene editing therapy for sickle cell disease, marking a major victory for gene editing in clinical applications. The recent approval for treating transfusion-dependent β-thalassemia further showcased the success of gene editing in clinical settings.

Recent research breakthroughs have allowed scientists to achieve precise and programmable large DNA sequence insertion. A team led by Gao Caixia at the Institute of Genetics and Developmental Biology of the Chinese Academy of Sciences published a study in Nature Biotechnology in April 2023, introducing the PrimeRoot system for efficient and precise insertion of large DNA segments in plants.

The PrimeRoot system, known for its high efficiency and accurate insertion of large DNA segments, holds the potential to enhance crops’ resistance to diseases and pathogens, driving innovation in CRISPR-based plant genome engineering. The technology is applicable to various plant species, offering promising prospects for agricultural advancements.

Other studies, such as the one by the teams led by Omar Abudayyeh and Jonathan Gootenberg at MIT in November 2022, also contributed to the development of gene-editing technologies, showcasing the Drag-and-drop genome insertion of large sequences without double-strand DNA cleavage.

Additionally, a team from Stanford University led by Le Cong introduced the dCas9-SSAP system in February 2022, providing a cleavage-free genomic knock-in of long sequences up to 2kb with high efficiency and precision.

Deep Learning for Protein Design

Advancements in protein design have significantly benefited from artificial intelligence. David Baker’s team at the University of Washington pioneered the use of deep learning for protein design. In February 2023, they created an artificial luciferase enzyme, a groundbreaking achievement using AI to design enzymes not found in nature.

Further, in April 2023, Baker’s team utilized AI based on reinforcement learning to design functional protein nanocapsules, opening new avenues for vaccine and drug delivery carrier development. In December 2023, they leveraged deep learning to design novel proteins with high affinity and specificity, paving the way for antibody design and disease diagnostics.

Brain-Machine Interfaces

The story of Pat Bennett, diagnosed with ALS in 2012, highlights the transformative potential of brain-machine interfaces. Participating in a clinical trial led by Professor Francis Willett at Stanford University in March 2022, Bennett had four microelectrode arrays implanted on her brain’s surface. The AI trained on her neural activity allowed her to communicate by converting her attempted speech into text.

The team led by Willett later published a study in August 2023, presenting a high-performance speech neuroprosthesis that achieved real-time conversion of Bennett’s neural activity into text at a speed of 62 words per minute, with an extensive vocabulary. This breakthrough offers hope for restoring language communication abilities in paralyzed individuals, especially those with ALS.

A separate study by Edward Chang’s team at the University of California, San Francisco, introduced a high-performance neuroprosthesis for speech decoding and avatar control, demonstrating the capability to simultaneously convert brain signals into text, speech, and avatar control for severely paralyzed patients.

These developments in brain-machine interfaces signify significant progress in neuroscience and neuroengineering, holding immense potential for alleviating the suffering of individuals who have lost their ability to speak due to paralysis.

Cell Atlases

The Human Cell Atlas (HCA) initiative, launched in 2016 by Sarah Teichmann of the Wellcome Sanger Institute and Aviv Regev, the current head of research and early development at Genentech, is a massive global effort involving nearly 3,000 scientists from around 100 countries. The initiative aims to create a comprehensive map of human cells, with sub-projects like the Human Biomolecular Atlas Program (HuBMAP) and the Brain Initiative Cell Census Network (BICCN).

While progress reports from the initiative have shown organ-specific maps generated using advanced techniques, Teichmann estimates that at least five more years are needed to complete the HCA project fully. Once accomplished, the human cell atlas will be invaluable, guiding tissue- and cell-specific drug development and enhancing our understanding of complex diseases like cancer.

Super-Resolution Microscopy

Since the Nobel Prize-winning breakthroughs in 2014 by Stefan Hell, Eric Betzig, and William Moerner, scientists have been pushing the boundaries of super-resolution microscopy. In 2022, Stefan Hell’s team developed the MINSTED method, achieving a resolution of 2.3 angstroms using specialized optical microscopy.

In 2023, Ralf Jungmann’s team at the Max Planck Institute for Biophysical Chemistry introduced the Resolved Emission Sequencing Imaging (RESI) method, enhancing resolution to visualize individual base pairs on DNA chains using standard fluorescence microscopy.

The ONE (One-step Nano Expansion) microscopy method, developed by a team led by Ali Shaib and Silvio Rizzoli at the University Medical Center Göttingen, offers nanoscale expansion capabilities, enabling direct imaging of fine structural details of single proteins and multi-protein complexes.

3D Printing of Nanomaterials

At the nanoscale, unique material properties emerge, allowing the creation of lightweight materials with enhanced strength, tailored interactions with light or sound, and improved catalytic or energy storage capabilities. Several strategies for precise manufacturing of such nanomaterials have been developed, with challenges including speed, material limitations, and costs.

Julia Greer at the California Institute of Technology presented an alternative approach, using hydrogels as microscale templates for 3D printing nanoscale structures, especially useful for materials like metals.

DeepFake Detection

The proliferation of generative AI algorithms has made the creation of convincing but entirely artificial images, audio, and videos increasingly simple. Professor Siwei Lyu, a computer scientist at the State University of New York at Buffalo, has witnessed numerous “Deepfake” images and audio related to military conflicts generated using AI. In response, Lyu and other digital forensics experts are focused on detecting and intercepting these deceptive creations.

One solution proposed by AI developers involves embedding hidden signals in the output of AI models, creating a watermark for AI-generated content. Other strategies concentrate on the content itself, identifying forged traces where one person’s facial features replace another’s. Lyu’s team developed an algorithm called DeepFake-O-Meter, which analyzes video content from different perspectives to identify “Deepfake” content.

These detection tools are valuable, but the ongoing struggle against misinformation and content generated by AI is expected to persist for years to come.

Nature Releases Seven Noteworthy Technologies in 2024

References:

https://www.nature.com/articles/d41586-024-00173-x

(source:internet, reference only)

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