Go with the flow – a new algorithm streamlines and improves flow cytometry analysis

Key points:

• A new algorithm developed by researchers at the Babraham Institute provides a fast and effective way to reduce errors in flow cytometry data analysis, overcoming a major restriction on harnessing the full potential of the power of flow cytometry in cell analysis.
• The tool, called AutoSpill, addresses the problem of overlapping signals and autofluoresence, which can be misinterpreted as genuine results.
• Researchers can use the tool, available online and through the software package FlowJo, to easily reduce compensation errors in their flow cytometry data.

Flow cytometry is a key investigative tool used in biomedical research, allowing researchers to identify, separate and study cells according to their characteristics, often working with cell samples containing millions of cells at an analysis pace of a million cells per minute. Cell identification is achieved by labelling cells with fluorescent tags. As with personal gadgets and devices, innovation in molecular biology technologies isn’t standing still. Advances in flow cytometry have allowed scientists to gather data on a growing number of parameters, simultaneously detecting over 30 different tags at a time to allow more sophisticated analyses and much deeper levels of insight. However, while flow cytometry equipment has been updated, the accompanying computational requirements have received less attention, until now. AutoSpill, an algorithm developed by researchers at the Babraham Institute and the VIB Center for Brain Research, brings data processing in line with state-of-the-art machines, simplifying data analysis and increasing accuracy. The new technique is published in Nature Communications today.

Immunology programme senior group leader Prof. Adrian Liston, explained: "Flow cytometry is a foundational technology across many different biomedical research areas, and is a key diagnostic tool in immunology, haematology and oncology. Despite the technical progress over the past decades, the technology has been held back by the mathematical processing of the data. Our new approach reduces error by 100,000-fold, making research and diagnostics more accurate. The collaboration with FlowJo has enabled us to instantly reach 80,000 users. It is very gratifying to see computational biology have a direct and real impact on research and diagnostics."

Using multiple fluorescent signals raises a key issue in flow cytometry called spillover. Spillover occurs because each tag, called a fluorophore, emits light within a range of wavelengths, giving it a unique colour. When multiple fluorophores are used, the signals begin to overlap. To accurately distinguish between two distinct fluorophore signals, researchers must process their data to compensate. Because flow cytometry uses so many different colour tags on each cell, the spillover between colours quickly accumulates, limiting scientists’ power to draw reliable conclusions from their results. The processing of data to remove the spillover between the different colours, known as compensation, is necessary for all flow cytometry experiments. Current methods require many hours of manual work, but AutoSpill reduces the process to minutes.

Dr Rachael Walker, Head of the Institute Flow Cytometry facility, commented: “The new AutoSpill Fluorescence Compensation algorithm is a great tool for quick, simple and accurate compensation. It allows compensation to be accurately calculated on samples where the traditional algorithm is difficult to use. AutoSpill’s integration into the FlowJo post-acquisition software highlights the importance of this new compensation method.”

Another limitation of flow cytometry is autofluoresence, fluorescence produced naturally by cells. The removal of these artefacts by AutoSpill is particularly useful for cancer biologists as tumour cells are high in autofluorescence, which can confuse identification of the type of tumour cell present. By solving these sources of error, AutoSpill can help remove false positives from cell analyses, ensuring more accurate data interpretations.

AutoSpill is available through open source code and a freely-available web service. AutoSpill, and a complementary related tool, AutoSpread, are also available in FlowJo v.10.7. Dr John Quinn, Director of Science and Product Development, FlowJo added: “AutoSpill & AutoSpread have been a revelation for FlowJo users. Compensation has long been one of the most perplexing aspects of cytometry, with the most critical requirement being pristine compensation controls collected for each and every parameter in an experiment. Overall, the combination of these two tools makes compensation both easier and more robust. As an indicator of the popularity of this new approach, the webinar held in conjunction with Nature to introduce AutoSpill / AutoSpread in FlowJo has been viewed over 400 times after the initial live event. We at FlowJo believe the AutoSpill / AutoSpread approach will be the primary means of approaching compensation moving forward.”

Battle Robots of the Blood reading

Me and Hayden read "Battle Robots of the Blood" together.

Researchers identify new genetic cause of severe immune disorder

Severe congenital neutropenia leaves young patients to contract infection after infection, leading to life-threatening situations. A team of Leuven scientists has identified a novel genetic mutation, pointing to a new causative mechanism for this severe immune disorder.

The story starts with patient Jane Doe, now 19 years old, but diagnosed with severe congenital neutropenia when she was just 2 years old. By that time, she had already suffered an ear abscess, recurring ear infections, bronchitis, sinusitis, tonsillitis and several gum infections.

After yet another infection, this time of her intestine, a detailed investigation revealed a striking shortage of neutrophils, white blood cells that are recruited as first-responders to the site of injury or infection within our body. Having an abnormally low concentration of neutrophils in the blood is referred to as neutropenia. When it is severe and present from birth (congenital), that is where the diagnosis of severe congenital neutropenia comes in.

“Severe congenital neutropenia is very scary, because these kids develop serious infections that can be lethal for infants,” explains Erika Van Nieuwenhove. “As if that’s not enough, they are also at increased risk for other conditions such as leukemia.”

Van Nieuwenhove is both an MD and PhD, who combines clinical work in the university hospital with Carine Wouters, with research at VIB and KU Leuven under the guidance of Adrian Liston and Stephanie Humblet-Baron.

Together with John Barber and several other colleagues, she set out to understand why Jane Doe developed SCN in the first place. Van Nieuwenhove: “For up to 50% of severe congenital neutropenia patients, we have no clue what causes the disease. It was the same for our patient, whose parents are both healthy.”

A new mutation in a familiar gene

After Jane Doe tested negative for mutations in all the genes with known ties to neutropenia, the researchers performed whole exome sequencing, probing every gene in the DNA, to trace back the genetic defect underlying the disorder.

“We identified a new mutation in a gene called SEC61A1, which encodes one of three subunits of the Sec61 complex. This molecular complex plays a crucial role in both protein transport and in maintaining the calcium balance of the cell,” explains Humblet-Baron. “Our experiments revealed that the genetic defect led to both a lower expression and a reduced efficacy of the SEC61A1 protein, and that these quantitative and qualitative defects in turn disturb neutrophil differentiation and maturation.”

Interestingly, SEC61A1 has recently been picked up in other studies that were not focused on neutropenia. Different mutations in the same gene were reported in two families with a rare kidney disease and in two additional families with an antibody deficiency.

“The fact that there are different mutations in the same gene indicates there may be overlapping mechanisms among the different disorders. With the low number of currently known patients, it is still too early to predict which mutations can lead to which symptoms,” explains Liston.

“What’s clear from our findings is that SEC61A1 mutations can also cause severe congenital neutropenia. Considering this gene’s link with other disorders, the clinical implications of our work reach far beyond the patient with whom it all started here in Leuven.”

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Read the original paper: Defective SEC61α1 underlies a novel cause of autosomal dominant severe congenital neutropenia. Van Nieuwenhove et al. JACI 2020

Dokter Algoritme

Algoritmen kunnen inzichten bereiken waar een mens moeilijk toe komt. Computeralgoritmen kunnen almaar beter moeilijke diagnosen stellen, soms zelfs beter dan artsen. Immunologe Erika Van Nieuwenhove van de Leuvense tak aan het Vlaams Instituut voor Biotechnologie (VIB) en haar collega’s melden in Annals of the Rheumatic Diseases dat ze een zelflerend algoritme hebben ontwikkeld dat met bijna 90 procent zekerheid artritis bij kinderen kan vaststellen, louter op basis van een bloedtest.

Het gaat om de vaakst voorkomende vorm van reuma bij kinderen, maar omdat de ernst en de evolutie van de symptomen sterk kunnen variëren, is een diagnose stellen niet altijd gemakkelijk. Het algoritme evalueert alleen de samenstelling van het immuunsysteem van de patiënten. Het zal nuttig zijn om te bepalen welke behandeling aangewezen is.

Knack - 24 Apr. 2019 - Page 86