The spark gap is the space between two electrodes. When a high enough voltage builds up, the gas between the electrodes lights up with a bright flash as electrons fly across. While we didn’t create any sparks during the experiments for the paper, the spark gap is a fitting analogy to how one of the ideas came about.
I think of the electrodes like two different scientific fields, both full of potential but not sufficiently close enough to allow much to flow between them. As scientists, it’s important to try and bridge this spark gap at every opportunity. We can reduce the distance between fields in many ways, but we also need to increase the potential in each field, something which is altogether more tricky. I think of increasing the potential as increasing the amount that each field has to gain or offer by working together.
At the Richter-Dahlfors lab in Karolinska Institutet where this work was done, there was the perfect opportunity to bridge the spark gap. The key was having a diverse group of researchers, from medical doctors to electrical engineers and everything in between. This could be a source of frustration when we didn’t understand one another, but the benefits were certainly far greater.
One day we were discussing the data analysis. We were wondering how to make sense of large numbers of spectra that were very similar but had subtle differences. The problem was, we had no precise way of comparing them. When discussing this with our colleague Susanne Löffler who has a background in signal processing, she said why don’t you do a principal component analysis?
My initial reaction was not entirely positive. I knew about principal component analysis, I’d done it a lot in my PhD analysing microbial communities. I remember learning about it from the ecology textbooks, species matrices, site matrices etc etc. Nothing about spectra in there. Then, after some gentle persuasion it hit me, the spark jumped across the gap. I understood that you could treat the spectra for each species as a matrix to perform the principle component analysis. This is why we use principle component analysis in this paper and why we continue to use it to analyse spectral data.
Figure 3 from the publication. More details here.
The thing that this taught us all is that just because something is obvious to us, it isn’t necessarily obvious to everyone. Feeling confident enough to speak to colleagues on a basic level and having enough patience to persevere with complicated conversation pays off. I believe that it is essential to increasing the potential utility of disparate fields and overcoming the spark gap.
This is my personal observation on the small role that I played in this paper. There are many more stories of course, like how excited people became when they received a particularly interesting (gross) urine sample or how frustrated everyone was when there wasn’t enough urine. Actually, most of the other stories around this paper involve urine, but they are not mine to tell.
The ability of uropathogenic Escherichia coli (UPEC) to adopt a biofilm lifestyle in the urinary tract is suggested as one cause of recurrent urinary tract infections (UTIs). A clinical role of UPEC biofilm is further supported by the presence of bacterial aggregates in urine of UTI patients. Yet, no diagnostics exist to differentiate between the planktonic and biofilm lifestyle of bacteria. Here, we developed a rapid diagnostic assay for biofilm-related UTI, based on the detection of cellulose in urine. Cellulose, a component of biofilm extracellular matrix, is detected by a luminescent-conjugated oligothiophene, which emits a conformation-dependent fluorescence spectrum when bound to a target molecule. We first defined the cellulose-specific spectral signature in the extracellular matrix of UPEC biofilm colonies, and used these settings to detect cellulose in urine. To translate this optotracing assay for clinical use, we composed a workflow that enabled rapid isolation of urine sediment and screening for the presence of UPEC-derived cellulose in <45 min. Using multivariate analysis, we analyzed spectral information obtained between 464 and 508 nm by optotracing of urine from 182 UTI patients and 8 healthy volunteers. Cellulose was detected in 14.8% of UTI urine samples. Using cellulose as a biomarker for biofilm-related UTI, our data provide direct evidence that UPEC forms biofilm in the urinary tract. Clinical implementation of this rapid, non-invasive and user-friendly optotracing diagnostic assay will potentially aid clinicians in the design of effective antibiotic treatment.
Rapid diagnostic assay for detection of cellulose in urine as biomarker for biofilm-related urinary tract infections
Haris Antypas, Ferdinand X. Choong, Ben Libberton, Annelie Brauner & Agneta Richter-Dahlfors. npj Biofilms and Microbiomes. Volume 4, Article number: 26 (2018)