Fundamental Research Update, September 2026

Biomarkers are measurable biological signals that can help researchers track disease activity, predict how a disease may progress, and evaluate whether a potential treatment is having an effect.

In ALS, one of the most promising biomarkers is neurofilament light (NfL), a protein released when neurons are damaged that can be detected in the cerebrospinal fluid and blood. Higher average NfL levels are generally associated with faster disease progression in large groups, and NfL is increasingly being used in clinical trials to help researchers understand whether a potential therapy may be reducing underlying motor neuron damage. However, NfL alone cannot fully capture the complexity of the disease or reliably predict disease progression at the individual level.

To address this challenge of more specific biomarkers, researchers are increasingly turning to large-scale protein analysis, known as proteomics. By measuring hundreds or even thousands of proteins in blood samples, they hope to identify additional biomarkers that could reveal what is happening in the body before symptoms appear and provide a clearer picture of disease progression.

Predicting disease onset

A recent study led by Dr. Michael Benatar (University of Miami) analyzed more than 500 blood samples collected over time from 137 participants, including people living with ALS, healthy controls, and people carrying ALS genetic variants. Among the genetic variant carriers, 33 individuals later developed signs of ALS or frontotemporal dementia (FTD). Data from study was in part collected through the Pre-fALS study, an important platform to study presymptomatic ALS.

Using proteomics, researchers measured over 5,000 proteins in the samples, resulting in the identification of 92 proteins that changed prior to symptom onset. The researchers then used machine learning to narrow their findings down to a panel of 19 blood proteins, including NfL and other proteins linked to different biological processes involved in ALS.

The researchers found this panel was better than NfL alone at identifying people who were getting closer to developing ALS symptoms and estimating when those symptoms might appear, from as little as six months to as much as five years before onset. On average, the model could predict symptom onset within about 18 months, representing a meaningful improvement over previous approaches.

Importantly, these findings also add to growing evidence that biological changes related to ALS may begin years before symptoms become noticeable. Previous studies have identified increases in neurofilament light chain (NfL), changes on brain imaging, and other biomarkers in people at genetic risk for ALS before they develop clinical symptoms, indicating that the disease has a presymptomatic phase.

Tracking ALS progression

In another recent study by researchers at University of Oxford, more than 1,000 blood and cerebrospinal fluid samples from over 400 people living with ALS were analyzed. Using advanced protein analysis, they identified dozens of proteins linked to survival, including NfL, as well as other proteins, such as peripherin, EDA2R, and calcitonin.

The researchers then developed a nine-protein biomarker panel that reflected both the extent of disease and how quickly it was progressing. This panel predicted survival more accurately than models based on clinical measures and NfL alone. They also found that changes in certain proteins over time, particularly EDA2R and calcitonin, were associated with survival, suggesting they should be further investigated if they can help track disease progression as ALS advances.

A third recent study also aimed at using proteomics to find better prognostic biomarkers to accompany NfL, identifying the proteins GSN and IGFBP2 as key targets for further exploration.

The takeaway

Biomarkers that can help predict ALS progression and measure disease activity are urgently needed. NfL remains a very valuable tool to support stronger clinical trial design and outcomes, yet it still has limitations.

These overall findings suggest that combining multiple biomarkers, rather than just NfL or clinical measures alone, may provide a more complete picture of how we can more effectively study, understand, and treat ALS.

Additional research and validation are needed before these protein signatures can be used in routine clinical practice. However, all three studies represent progress toward more precise mapping of the biological changes associated with ALS, from the earliest stages before symptoms appear through to disease progression after diagnosis.

*A common feature in most ALS cases is a disruption of the normal function of a protein called TDP-43. This protein is found primarily within the nucleus of a cell, where it helps to regulate essential cell processes, but in ALS, it becomes dysfunctional and gets trapped outside in the cytoplasm, forming clumps or aggregates. These clumps, and the loss of TDP-43 cellular function, are theorized to contribute to motor neuron damage and death. Because of this, treatments aimed at restoring lost TDP-43 function or clearing TDP-43 clumps could offer a possible way to slow or stop ALS progression.

Researchers are continuing to investigate new ways to target TDP-43*, a protein that becomes dysfunctional in the vast majority of ALS cases. This study examined whether blocking an enzyme called TTBK1 (tau tubulin kinase 1), which helps modify TDP-43, could reduce changes related to the disease and protect motor neurons.

Why is TTBK1 important?

TTBK1 is an enzyme that can add phosphate groups to TDP-43, a process called phosphorylation. Abnormally phosphorylated TDP-43 is commonly found in ALS and related neurodegenerative diseases, making TTBK1 a potential therapeutic target. If TTBK1 activity can be safely reduced, some researchers theorize it may limit harmful TDP-43 changes.

What did this study find?

In this study, researchers developed a series of selective TTBK1 inhibitors designed to enter the brain while avoiding related enzymes that could cause unwanted side effects.

The researchers found that these compounds reduced levels of phosphorylated TDP-43 in cell models in the lab, including cells derived from people with TDP-43-related neurodegenerative disease. They then tested a lead compound in a mouse model showing TDP-43 abnormalities. Treatment reduced TDP-43 pathology, decreased activation of inflammatory brain cells, protected neurons in the frontal cortex, and improved cognitive performance.

The takeaway

This study provides further evidence that TTBK1 could be a promising target for diseases characterized by TDP-43 pathology, including ALS. By reducing abnormal TDP-43 changes and showing protective effects in a mouse model, the findings support continued research into TTBK1. However, this work is still at the preclinical stage, and additional studies will be needed to determine whether these findings translate to people living with ALS.

*ALS Canada is a proud supporter and has contributed close to 1,000 Canadian samples to Project MinE.  

In this study, researchers explored a new artificial intelligence (AI) tool designed to look for genetic patterns linked to ALS. Using genetic data from more than 47,000 people across 13 countries, including data from Project MinE, the team trained a deep learning model to recognize combinations of genetic changes associated with the disease. Unlike current genetic tests, which focus on a small number of known ALS genes, this approach aims to capture the many smaller genetic factors that may contribute to ALS risk, particularly in people who do not carry a known ALS genetic variant.

When tested on independent datasets, the model was able to distinguish between people living with ALS and unaffected controls with reasonable accuracy, outperforming several traditional genetic risk prediction methods. The researchers suggest that, with further validation, tools like this could one day help identify people at increased genetic risk of ALS and support diagnosis alongside existing clinical and genetic assessments.

Importantly, this technology is still in the research stage and is not ready for clinical use. The study primarily included people of European ancestry, and the model cannot predict with certainty whether an individual will develop ALS. However, the findings highlight how AI may help researchers better understand the complex genetics of ALS and improve future approaches to genetic risk prediction.

A new study reported that immune cells in the brain and spinal cord, called microglia, may contribute to motor neuron loss in a mouse model of ALS. The researchers identified a pathway involving TAM (Tyro3, Axl, and Mer) receptors that may prompt microglia to recognize living motor neurons as targets and consume them, a process that would normally help clear damaged or dying cells.

Microglia and other immune processes have been linked to ALS for more than two decades, and researchers increasingly recognize that inflammation plays an active role in disease progression. However, translating this knowledge into effective treatments has proven challenging. This study highlights TAM receptors as a potential new pathway through which microglial activity might be altered, raising the possibility of new therapeutic approaches.

However, because this research was conducted in mice, further studies in people living with ALS are needed to determine whether the same process occurs in humans and whether targeting this pathway could safely influence disease progression. Importantly, the study does not show that microglia are the primary cause of ALS or that blocking this pathway would be an effective treatment yet. In fact, removing the proteins involved produced both beneficial and harmful effects in the mice, highlighting the complexity of these immune and inflammatory processes and the need for further research.

Acute viral infection and ALS progression in mice models

In this ALS Canada funded project, Dr. Art Marzok and researchers at McMaster University explored how viral infections affect ALS in a mouse model of the disease. The researchers found that a single respiratory viral infection accelerated disease progression, even after the virus had been cleared, by further increasing the inflammatory response, resulting in more loss of motor neurons. Treatment with antiviral and anti-inflammatory therapies helped reduce these effects, suggesting that infection-related inflammation may influence how ALS progresses. More research is needed to understand what these findings mean for people living with ALS, particularly as anti-viral and anti-inflammatory treatments have been as-yet unsuccessful in changing the course of the disease.

Can smartwatches help monitor ALS progression?

A new ALS Canada supported study led by Dr. Gordon Jewett and colleagues at the University of Calgary evaluated whether consumer smartwatches could be used to track physical activity in people living with ALS. The researchers found that smartwatches closely reflect overall activity levels and disease changes over time, supporting their potential use for remote monitoring in ALS research and clinical studies. changes over time, supporting their potential use for remote monitoring in ALS research and clinical studies.

With further validation, wearables research could help reduce the burden of frequent clinic visits as mobility and function change over time, expand access to monitoring for people in rural or remote areas, and provide more continuous information about disease progression than occasional in-person assessments alone.

Brain changes linked to speech difficulties in ALS

A new study from the Canadian ALS Neuroimaging Consortium (CALSNIC), examined how changes in speech relate to changes in the brain in people living with ALS. The researchers found that thinning in brain regions involved in speech production was associated with slower speaking and articulation rates. Interestingly, these changes were detected even when more traditional measures, including the ALSFRS-R bulbar score and standard neurological examinations, did not identify clear differences. The findings suggest that speech-based measures, such as speaking rate and speech clarity, may provide a sensitive and non-invasive way to monitor bulbar dysfunction and track disease progression over time.