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Slaughterhouse.ac | cattle walks to the slaughterhouse happy

CH_cow_2_cropped
https://en.wikipedia.org/wiki/Cattle CC BY-SA 3.0

Although I am no expert in livestock production and food chains, I do recall debates on making abattoirs more humane by ensuring that animals are not aware of their fate. In other words, the poor bovine should not see other fellow animals being slaughtered fearing for their lives in a long and slow progressing queue towards death. Fair enough.

 

***

While travelling to London for a networking event, I was messaging over Slack with a friend, a former PhD student, and casually chatting about a number of things. At his question: ‘how things are going’, I instinctively responded along the line of ‘well, although growing tired of the slaughterhouse that is academia…’. Although I love Academia, I have been also openly critical about it over the years. However, I never defined certain processes of Academia as a ‘slaughterhouse’ before. At the same time, the definition fits so well.

***

When I was a PhD student, I thought that for those students like me, doing anything else than staying in Academia was a failure. Bovine-me was roaming the green fields of Germany and The Netherlands happily fattening (quite literally in my case). A constant flow of fellow students would join us in ever greener pastures, cohort after cohort, and many others leaving for higher hills never coming back, with a few – barely visible at a distance – growing older at one of the most remote fields. Bovine-friends say – fields where the grass is the sweetest.

When the time came, and the gates opened, we rushed to the next wonderful field. Who did not rush, was simply pushed by the flow of the pack. Despite the dynamic crowdedness of lower fields, bovine-us kept decreasing in number happily walking towards the gates opening towards those greener and sweeter pastures we always fantasized about.

Most of us, fat and strong, perfect bovines, queued for the next gate, happily walking to an even better field, one-by-one, blissfully unaware, a pop, the last memory. Others are still grazing.

***

Academia depends on the constant flow of students through their classes, and many Universities, no doubt about those I know, do a wonderful job in training them. So many committed people that are dedicated to passing and expanding  knowledge down the generations. Academic research depends on a rather large cohort of PhD student and post-doctoral scientists working hard, often paid modestly considering the years of unpaid (or worst debt-causing) training lured into the next job by promises of stability but kept in an unforgiving state of precariat. Short-term contracts after short-term contract in a job where long-term vision should be key, we are subject to a constant process of review that in the best case is rigorous and tough, but that can be often also quite random and biased.

This process is largely physiological as the academic system is very competitive. Many collogues also express no concerns about it on the basis that selection have to occur, in a way or another. However, the impact on the mental health of academic workers is now evident, and not just only on students. I believe that a more efficient and fair system would be one that promote leaving academia early as an active choice, where different career options are promoted, where there is clarity about the likelihood of promotions, and where there is no choice to be made between having a family and having a job.

Just to clarify the last point, once I was in a leadership course. A colleague asked “My husband has a tenured position at Cambridge University and we have a child. I am offered a tenured position elsewhere and I see no opportunity at Cambridge, what should I do?” The reply was: “I guess you have to make a choice between family and career”. As horrible as an answer it was, I should also clarify for those that are not aware of it that for who works in Academia this is not a choice, as if you do not progress on the ladder of academic positions, it is likely one day the gate of Slaughterhouse.ac will open for you – pop.

I hope one day, would I survive or strive in the system, I will have the tools to influence it and change it for the better. For now, I can just write about it, hoping that younger scientists will make more informed decisions than me and most of my colleagues. ■

A ‘hyper-dimensional radio’ to listen to the biochemical communications of the cell

hdim_resIndustry, academia and healthcare often rely on fluorescence microscopy to see the fine architecture of materials, including biological ones. Fluorescence microscopy is particularly suited for biomedical studies because it can be gentle with biological materials permitting investigators to study biology in a non-destructive manner. Chemistry and genetic engineering then provide useful strategies to make samples fluorescent so to report about mechanisms that we need to study aiming to understand how biological systems work in normal conditions, during disease or therapy.

Thanks to two-decades of fast-paced innovation in fluorescence microscopy, we can now see the smallest features of a biological sample, approaching molecular resolution. However, the capability of fluorescence microscopy to observe small changes in the chemical or physical properties of biological samples is not as well-optimised as its capability to peek into small structures. In our recent paper entitled “Enhancing biochemical resolution by hyper-dimensional imaging microscopy” – now available at the Biophysical Journal – we demonstrate how to recover information that permits us to make better measurements.

We can think of a fluorescence microscope like a radio broadcaster that transmits useful information through different radio channels. When we listen to one individual radio channel, we lose information transmitted over the other frequencies. If we attempt to listen to several broadcasts at the same time, the scrambled voices will limit our understanding of the several messages that were originally broadcasted. Similarly, the lasers we use to make samples shine, and the fluorescence emitted by samples, transmit information spread over the different properties of light, for example in its colour, in the time when light is emitted (the fluorescence lifetime) and in which plane is vibrating (polarisation).

In our recent work, we describe theoretically and experimentally how all this information could be measured separately but simultaneously enhancing our capabilities to observe biological processes. By breaking conceptual barriers and showcasing possible technological implementations with hyper-dimensional imaging microscopy, we aim to catalyse advances in several applications, spanning material sciences, industrial applications, basic and applied biomedical research, and improved sensing capabilities for medical diagnostics.

The New&Notable commentary by Prof. Suhling on Biophysical Journal

Our open-access work on Biophysical Journal

At FLIM impact (Episode I)

What has been the impact of fluorescence lifetime imaging microscopy to science and to the biomedical community in particular? Is FLIM a niche technique, one of those techniques that always promise but never deliver?

The top 10 most cited papers

Excluding reviews, the list of the top 10 most cited papers, albeit representing a very narrow window on the impact that FLIM had on the broader community, is rather instructive. Do consider, we are missing all those papers where FLIM was used but not cited in title or abstract. Most of the top 10 is made of applications to cell biochemistry, demonstrating the potential and the impact that fluorescence lifetime has. FLIM helped to understand how signalling work in living cells and animals, helped to identify drugs and to study disease. Some of the top cited papers are more technical, such as Digman’s paper on the phasor-transform or Becker’s paper on TCSPC widely cited because of their influence on contemporary FLIM techniques from a perspective of data analysis and technology. Other papers date back to the initial years of FLIM with applications to biochemistry. Overall, from this list, we understand (if more evidence was needed) that FLIM had a deep impact on the understanding of cell biochemistry albeit, historically, FLIM has been limited to the specialist laboratory.

I would like to highlight also another small observation, perhaps just interesting for the specialists, and not visible from other bibliometric analyses. Tom Jovin and a group of scientists trained by him (e.g., Dorus Gadella and Philippe Bastiaens) left a significant footprint in the field, directly driving biomedical relevant applications while pushing, at the same time, technological or methodological developments. Many others are linked to this ‘school’ directly or indirectly, scientists that use/develop a microscope to do biochemistry.

  1. Mapping temperature within cells using fluorescent polymers by Okabe and colleagues (2012) from Uchiyama’s laboratory and published in Nature Communications, where FLIM was used to map temperature within cells using fluorescent polymers as temperature sensors. (442)
  2. Phasor analysis by Michelle Digman and colleagues, from the laboratory of Enrico Gratton (2008) published by Biophysical Journal. The phasor-based analysis, in different flavours, has become quite popular nowadays. (406)
  3. An in vivo FLIM-based analysis of calcium dynamics in astrocytes by Kuchibhotla and colleagues from Bacskai’s laboratory (2009) published in Science. (353)
  4. The study of Calmodulin-dependent kinase II activity in dendritic spines by Lee and colleagues from Yasuda’s laboratory (2009) published in Nature. (351)
  5. One of the first FLIM papers by Lackowicz, published in 1992 in PNAS, where they applied the methodology, yet to be fully established, to the study of free and bound NADH. (339)
  6. One of the first biochemical applications of FLIM, where Gadella and Jovin applied the new tools to the study of EGFR oligomerization (1995), published in the Journal of Cell Biology. (323)
  7.  A 2004 paper, where Becker and colleagues present the TCPSC instrumentation that would become a commercial success, published in Microscopy Research and Technique. (321)
  8. The application of FLIM and molecular motors to study viscosity of the cellular environment by Marina Kuimova and colleagues, from the laboratory of Klaus Suhling published on JACS in 2008. (319)
  9. The development of a drug interfering with the interaction between KRAS and PDEdelta published Zimmermann and colleagues with the laboratory of Philippe Bastiaens and published by Nature in 2013. (291)
  10. The interaction between PKC and integrin shown by Ng an colleagues from Parker’s laboratory in 1999 by the EMBO Journal. (277)

Methodology

Tool: Web of Science

Search term: “FLIM” and “fluorescence lifetime imaging microscopy”

Filter: Article

Note: FLiM is a component of the flagella motor and it shows up in the searches. I could not eliminate this ‘false positive’ but it is my assumption that it is not changing  the following discussion.

Citations (in parenthesis) as in April 2019.

Any bibliometric analysis is very limited in scope, certainly this very narrow search. This is just a blog post, one observation done just to trigger a discussion for those curious people about the topic.

 

Snap opinion on deep-learning for super-resolution and denoising

I am personally conflicted on this topic. I have recently started to work on machine learning and deep-learning specifically. Therefore, I am keen to explore the usefulness of these technologies, and I hope they will remove bottlenecks from our assays.

My knowledge about CNNs is rather limited, even more so for SR and denoising applications. My first opinion was not very positive. After all, if you do not trust a fellow scientist guessing objects from noisy or undersampled data, why should you trust a piece of software? That appeared to be also the response of many colleagues.

After the machine learning session at FoM, I partially changed opinion, and I am posting this brief -very naïve – opinion after a thread of messages I read on twitter by colleagues. Conceptually, I always thought of machine learning as ‘guessing’ the image, but suddenly I realise that CNNs are perhaps learning a prior or a set of possible priors.

I have mentioned in a previous post about the work by Toraldo di Francia on resolving power and information, often cited by Alberto Diaspro in talks. Di Francia, in his paper, states “The degrees of freedom of an image formed by any real instrument are only a finite number, while those of the object are an infinite number. Several different objects may correspond to the same image. It is shown that in the case of coherent illumination a large class of objects corresponding to a given image can be found very easily. Two-point resolution is impossible unless the observer has a priori an infinite amount of information about the object.”

Are CNNs for image restoration and denoising learning the prior? If so, issues about possible artefacts might be not put aside but at least handled a bit better conceptually by me. The problem would then shift to understand which priors a network is learning and how robust these sets are to typical variations of biological samples.

Great talks today at FoM. Eventually, we will need to have tools to assess the likelihood that an image represents the ground-truth and some simple visual representation that explain what a CNN is doing to a specific image that is restored and ensure good practise. Nothing too different from other techniques, but I feel it is better to deal with these issues earlier rather than later in order to build confidence in the community.

Related twitter thread: https://twitter.com/RetoPaul/status/1118435878270132225?s=19

This is what happens when you attend Focus on Microscopy for a few years…

Most of the times, I write this blog for those two youngsters that might learn something by accidentally landing here. I wished to share with you a few things that might happen when you age, at least academically speaking. This is what happens when you attend FoM for a few years…

***

I have met my PhD supervisor, Hans Gerritsen, a scientist and a man I greatly respect. Lost in memory lane, I have (re)told the story of when – while working in Goettingen with Fred Wouters – I wished to update Hans with a report on my latest theoretical developments.

Right click…

Create folder…

“this destination already contains a folder named ‘saturation FRET’ “

Well, I had discovered I had already written several tens of pages of maths for Hans, for then completely forgetting about it!!!

Which is the point? Well, I do have a horrible memory! I always had.

***

When you get older you get many stories to tell and I like to tell stories having a laugh. When I meet people I got to know in the past and that have longer experience than me, I like to ask more historical accounts of the early times of, for instance, FLIM developments. Not a long time ago, I had a wonderfully entertaining and instructive conversation with Peter So and Ammasi Periasamy walking in the historical streets of Venice after lecturing at an international school of microscopy organized by Alberto Diaspro. Lots of fun, for me at least, speaking about the various characters of the field, anecdotes,  reconstructing the ‘genealogy’ of the various innovators (how they are scientifically related to each other). And I could not resist asking the question: “who did the first FLIM image”? I suggest, Wang et al 1989, but I am uncertain as I was 14 back then 🙂

Did I tell you I have a bad memory? Well, I did not lose the opportunity to re-ask to Hans, when I met him at FoM, “who did the first FLIM image”? Possibly, Chris Morgan.

While ‘googling’ Chris Morgan I found my own paper on Lifetime Moments Analysis (LiMA), as I cited his work. Well, DID I TELL YOU I HAVE A BAD MEMORY?

I also ‘discovered’ I did write a brief paragraph as a historical introduction about FLIM, and I had a flash-back of me asking Hans in 2005 the same question, which probably places Bugiel, Konig and Wabnitz as the winners in 1989. But let me know if you really know who published the first FLIM paper 🙂

***

Here we are, a very new thing I have just noticed about what happens when you age academically. Presumably, the first FLIM paper was published in 1989, with work on FLIM proliferating during the first half of the 90s. My first paper on FLIM is the LiMA work of 2005 published in Biophysical Journal, ‘only’ 16 years after the first FLIM paper. Yesterday, my latest contribution to the field just got accepted on Biophysical Journal, 14 years later. Although I do not work full-time on it, I have contributed to the development of FLIM, in one way or another, for almost half of the time that FLIM exists. This gives me a rather strange feeling.

It is very instructive, in my opinion, for students and even for a tiny bit older ‘students’ like me to pause for a moment and look to the past of their discipline or the technologies they use. Compared to Physics, for instance, cell biology, biophysics, cancer biology are all rather recent disciplines. Fluorescent proteins are on the map since the sixties, but usable only since mid-90s not so long time ago for instance. Imagine what we might be able to do in another 20 years.

***

FoM is an occasion to meet many people, peek in the future through the talks of fellow scientists and discussions, watch back to past memories. Yesterday I barely walked three lines of posters in 2 hours, as I was getting engaged in interesting conversations every few meters with people I just met or people that I know, in a way or another, since many years. When I called back my wife, Suzan, in the evening, she reminded me of my first FoM in Australia, when I called her saying I was feeling a bit lonely and awkward as I did not know anyone.

Conferences like FoM are community, history, a boiling pot of ideas. I have been always a bit shy, and my suggestion to younger scientists is to make an effort not to be. Engage the others. Working in academia can be rather frustrating at times, and feeling part of a community can really help you in the future.

***

Fourteen years from FLIM paper to FLIM paper in Biophysical Journal. When not affected by an attack of imposture syndrome, I look back and I feel good in seeing what I have done so far. However, there is yet another thing I discovered when you age academically. The legacy one person builds is not papers. In part is the reverberation of your work in those of others, irrespective of explicit citations. In part is the comments of colleagues who tell you, even just privately,  when they got inspired by something you said, presented or published. But, growing a tiny bit older every year with FoM, it is also the younger generations coming to speak to you.

And, I would like to thank you all, because while impact factors, panels, research outcomes are the fog in which someone might lose themselves a bit too often, you are the light at the horizon signalling we are, after all, walking the right direction.

Our superpower is you (first draft)

This is a preliminary draft for a leaflet aimed at outreach events. Following the work we have done on Women in STEM at the last year Cambridge Science Festival, this year we’ll add a second leaflet to be more inclusive. This will be a word search puzzle that we will release into the public domain for your peruse. The idea is to have a list of people to include different genders, ethnicities, sexual orientations and disabilities. While my team is working on the graphics and the production of the leaflet, I prepared the first draft of the name list. As I believe it can be certainly improved  I wished to ask for suggestions and criticism.  

The message: “There is not much in common among these inspirational people, no colour, no gender, no physical ability. Hard working and smart, nothing else defines a scientist. A scientist is someone like you.”

Alan Turing (1912-1954),  Mathematician, a founder of Theory of Computation

Florence Nightingale (1820-1910), Social reformer and statistician, the founder of modern nursing. nurse

Neil Divine (1939-1994), Astrophysicist, a major contributor to the modern theory of
star formation.

Lynn Ann Conway (1938), Computer scientist and electrical engineer. Pioneer in electronics and computing.

George Washington Carver (1860-1943), Agricultural scientist and inventor. Promoted alternative crops to cotton and methods to prevent soil depletion.

Ernest Everett Just (1883–1941), Biologist, a pioneer in the studies of fertilization and early development,

Stephen Hawking (1942-2018), Theoretical physicist and cosmologist, a pioneer in the modern theory of cosmology and black holes.

Edwin Krebs (1918-2009), Biochemist. Pioneering work on post-translational modification of proteins and cell regulation.

Albert Einstein (1879-1955),  Theoretical physicist, founder of the theory of relativity, one of the two pillars of modern physics.

Charles Darwin (1809-1882), Naturalist, geologist and biologist, a pioneer in the science of evolution.

Rita Levi-Montalcini (1909-2012), Neurobiologist, pioneering discoveries in neurophysiology.

Chien-Shiung Wu (1912-1997), Experimental physicist who made significant contributions in the field of nuclear physics.

The design of the leaflet will be based on a diverse group of superheroes, with the message “Our superpower is you” meaning that science main resource is one: people.

 

 

The backstage story of a paper. Highs, lows, lessons to learn

Since a few months, the manuscript entitled “Multiplexed biochemical imaging reveals caspase activation patterns underlying single cell fate“, and authored by Maximilian W Fries, Kalina T Haas, Suzan Ber, John Saganty, Emma K Richardson, Ashok R Venkitaraman, Alessandro Esposito, is available as pre-print at the bioRxiv repository. It has started its journey through the peer-review process, but here I wished to explain to students and young scientists what happened behind the scenes as, I believe, can be instructive.

The inception of the idea | I am unsure if it will be evident from the manuscript, but this is the culmination of a huge effort that started more than a decade ago. I was about to leave the Cell Biophysics Group led by Prof. Fred Wouters after I completed my PhD, on a train from Goettingen to Bonn where my partner used to work,  thinking: “What should I do next? … something that while capitalizing on my training can make my work distinct from my mentors and others? Where can I have the highest impact?” Moment that stuck in my memory.

I believe I read Santos et al. (2007) “Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate.” in that period, a paper that influenced me significantly. It made me thinking of cells as if they were computational machines, interpreting various inputs from the extra- and intra- cellular environment to trigger appropriate outputs, cell states or transition between cell states, i.e. cellular (fate) decisions. Everyone working with microscopy knows that cells treated equally often behave differently and, therefore,  I started to formulate ideas around the following question: “How does a network of biochemical reactions encodes for cellular decisions? Why do genetically identical cells take a different decision faced by a similar stimulus?” Basic principles, the science I love the most, but questions worth answering also to obtain mechanistic insights, questions also quite relevant to disease.

As a matter of fact, it is  of fundamental importance to understand how cells trigger pathological states or if differences in biochemical networks can be used as diagnostic markers for patient stratification or targeted for therapy, concepts that I started to work only later. Certainly, I thought back then, with my unique blend of physics, engineering, mathematics, molecular and cell biology I can do, in this area, what others might not be able to. Therefore, since 2007, my aim is to image not just a biochemical reaction, but biochemical networks within intact living cells, while they undertake decisions.

Finding the resources, the initial success | Perhaps other students start less naïvely than me, but soon I would discover that having a good idea (let’s suppose it is a good idea) and having the right skills is only a tiny part of the job. First, aiming to coordinate my work with that of my partner (now wife), I accepted a job offer at the University of Cambridge to work with Prof. Clemens Kaminski and Dr. Virgilio Lew to study one exciting but quite unrelated project. While working on the homeostasis of P. falciparum infected red blood cells, I set up collaborations and wrote an EPSRC fellowship which was funded. Therefore, in 2009, two years after my first idea, I got the funding to work on biochemical multiplexing. With this fellowship, I was able to refine my expertise in biochemical multiplexing, permitting me to build advanced technologies for fluorescence sensing such as confocal  spectro-polarimetry and fast SPAD-based spectral FLIM. This EPSRC fellowship, together with my expertise and vision, and the benefit to have already established my name in the community thanks to the work I had done with and the support of Prof. Fred Wouters and Prof. Hans Gerritsen, were an excellent platform that permitted me to do the next jump and accepted a senior position at the MRC Cancer Unit.

Finding the resources, the struggle |  Rather than focusing just on technology, I then broaden my research to a research program that would require theoretical developments, engineering of new pairs of fluorescent proteins to achieve multiplexing, coding and, of course, biological applications. I recognize that expanding my research before seizing the appropriate resources was a significant mistake or at least a huge risk. Working within Prof. Ashok Venkitaraman group, I started to write ambitious EU grants. Some of them would receive excellent feedback (14 out of 15 points, first or second not funded…) but fall short of being funded. Hans once told me that “at this level of competition and quality, often it is just noise that decides the final outcome“. Probably true, even funny if you knew we worked together on photon-statistic (‘noise’). But great feedback does not replace funds, and thus I wrote an ERC grant.

I did not get ERC funding but, once again, ERC is very competitive and I was not sufficiently experienced, thus no drama. However, I started to notice one big issue. Physicists would judge my physics not great physics, biologists would judge my biology not great biology. Some colleagues would find my objectives impossible to reach. This is what I have then discovered to be the challenge of doing multi-disciplinary research (well, technically is called trans-disciplinary research, but this is the topic for another post). When your proposal is both trivial and impossible, you might have an issue that is not necessarily related only on your science. One referee commented that “A number of groups have being trying to improve the technologies for many years and although some of them have an enormous experience they are not anywhere close to where he intends to be in five years“. Around the same time, a renown scientist commented on the description of my work “It is impossible”, but then added in a wonderfully supportive and very appreciated manner “but if there is someone that could do it, it is Alessandro” – well, if funding-proposals could be judged with the human touch that people have when speaking in person knowing and respecting each others work…  I’ll cut an even longer story short, but with significantly less resources than I was asking and struggling to increase my funding, with the financial backing of Prof. Ashok Venkitaraman, we did everything we wanted to do in… five years!

The great technical success (NyxBits and NyxSense) | I wished to tell you a story of great success in a broader sense, but this has to be still written… if it will ever be. I did waste significant amount of time in looking for resources in what I found an amazingly inefficient system. However, from the end of my EPSRC fellowship since this year (~6 years), we have done a huge amount of work to realize what it was thought not to be possible:

  • Molecular Biology. I wished to develop two platforms, one based on spectrally multiplexed time-resolved anisotropy (open for collaborations here!) and one for spectral FLIM to manage the cross-talk between multiple FRET pairs and making biochemical multiplexing possible. With the limited resources I had, and initial help from Bryn Hardwick, Meredith Roberts-Thomson and David Perera in Ashok’s lab, we kick-started the project. The mole of work started to overwhelm me. Occupied with grant writing, training in a new field, engineering, software development and mathematics, I could not push this forward as fast as I wished. A great help then arrived from Max Fries who did 6 months with me as master student. Once he left, I was short of resources again, with the FRET pairs misbehaving and exhibiting aggregation or spurious signals, we abandoned one of the two sensing platforms.  Emma Richardson then joined me as a Research Assistant dedicated to cloning and testing FRET pairs and then Max came back to work with me for another four years as a PhD student. Committed and skilled, he tested tens and tens of FRET pairs. The work was a huge task, but a couple of paragraphs in the manuscript. We even have better pairs then we used in this work, all described in the Supporting Information. Indeed, under the pressure for publishing on high impact journals, I decided (probably anoher mistake of mine) to progress to applications, settling for what we recently baptized as NyxBits: mTagBFP, sREACh, mAmetrine, msCP576, mKeima and tdNirFP, so to focus on biological applications. NyxBits and NyxSense? Well, I have explained the choice of names elsewhere.
  • Mathematics and software. There is something I could not really write in the manuscript so explicitly and it is appreciated only by the experts in the field. There is something I also find impossible to communicate to review panels. As a testimony to this, I report here a comment I was once relayed to, something like: “Why do we need to offer him a carreer, once he has built the instruments we really need one person just clicking a button, no?” (I am sure I remember it much worst then it was. May be). The integration of technologies is so new and challenging, that we had to formulate new theoretical frameworks and write all new software, including how to acquire data, data format, and analysis. Also, some aspects of our work are difficult to appreciate. Let me tell you another small event that would push me in a particular direction. I really enjoy the conference Focus on Microscopy, even when criticized. Presenting new ideas, a colleague – respectfully – questioned the possibility for multiplexed imaging to be capable to measure several FRET pairs at the same time. This stimulated me to resume studying the Fisher information content in biochemical imaging. What is the biochemical resolution in microscopy? Can we enhance it? After years of thinking about this topic, in 2013 I cracked the problem, and published the mathematics in PLOS ONE where I formulate what I defined ‘the photon-partitioning theorem’. Then, with the increasing financial backing of my Director, Kalina Haas joined my growing team. Kalina implemented unmixing algorithms  and complex data analysis pipelines. Max and Kalina then became my dream-team to progress the project to the shape you can read today.
  • Technology. I mentioned some earlier technology platform that were designed for biochemical multiplexing. In my recent and first release of manuscripts on bioRxiv, we  also published a full implementation of Hyper-Dimensional Imaging Microscopy (HDIM)  with which we backed the photon-partitioning theorem with experimental evidence. We have done much more in that direction, but when we started biological applications, we realized the need for faster FLIM systems. Uncapable to wait for commercial solutions or to gain the benefits of other prototypes we had developed, I decided to build my own fast multiplexed electronics. This development was fostered by a negative criticism of a referee. During a PNAS submission of our spectral FLIM system, a referee mentioned we could do the same utilizing Hybrid PMTs. I disagreed, as achieving 64 channel spectral FLIM with the capability to run at hundreds of millions of photon-counts per second is all-together a very different application; however, there is merit in most referees’ criticisms, even the most negative ones. Only then I have realized PMT are now very fast and the bottleneck was just the electronics. Therefore, I got in touch with Surface Concept  who supported me wonderfully and  sold me one of their multi-hit TDC platforms. After several months of software development, we were then capable to run FLIM measurements with the quality of TCSPC and the speed of FD-FLIM. As usual, I presented this work at FoM where it was greatly received by colleagues and companies, but we did not publish the imaging platform as we were fully committed to pursue biological applications.
  • The biology. The bottleneck of our experiments was and still is data analysis and, with tens of experiments, thousands of biochemical traces to be painfully manually curated, we moved ahead very slowly, but working hardly. Mostly Max, Kalina and myself, suffered years of hard work, the occasional worry when something stopped working, and the excitement of seeing things that others could not see, for the first time. In this manuscript, we reveal the extent of non-genetic heterogeneity that  biochemical networks can exhibit and that eventually result into difference cellular decisions. Here, we focused on multiplexing simple biosensors for caspases as we aimed to de-risk and very ambitious project. We also decided to work with HeLa cells, again for the same reason. Despite the simplicity of the model system under study, we realized how complex and heterogeneous the response of biochemical pathways is, the cross-talk between enzymes, signaling pathways and cellular metabolism. All of this is, for me, fascinating and it shows that whenever we do ensemble measurements, we really see only the behavior of the average cell. It is then important to understand that the ‘average cell’, most of the times, does not really exist. If we are lucky, the bulk of the population responds with one phenotype and the measured ‘average cell’ will indeed represent the ‘most frequent cell’. However, in other instances when there are significant populations behaving in distinct ways, we would not just miss important information. The model inferred from the ‘average cell’ would be simply the wrong model of a non-existing cell. This is why it would be important to know, for any assay, if the sample behave synchronously with a stimulus and homogeneously. In this sense, single cell biochemistry, could bring not just an additional layer of information, but inform us if what the observations we obtain on a given model system with ensemble measurements can be reliable.

Enduring the struggle | I hope you did not mind I spoke so positvly about my own work. If you know me, you also know I am not so self-centered. However, I wished to let the younger scientists to know what there might be between a ‘good idea’ and its realization, passing through frequent failures and some success. Probably, one of the most precious quality of a scientist is resilience. We need thick skin to confront the constant failures that lead us to discoveries, the constant struggles in getting resources and eventually publishing good work in a highly competitive environment. Turning a negative event in something negative is part of this process. Understanding why one experiment did not work enables us to make troubleshooting, why an experiments falsified our hypothesis to build new and better models, why funding was not awarded or a manuscript was not published how we can improve our scientific proposals and reporting. Of course this is easier said than done.

The work we presented in bioRxiv is not the end of the story. The work, wonderfully-received in conferences, is still not peer-reviewed. Will colleagues appreciate and understand the vision of our work, its possible impact and the mole of work we had to do? Were we able to communicate properly? And even if we did it, we still have a long way in front of us. My dream is to establish a single cell systems biology of cell fate. A huge amount of work, from maths to biology, from biotechnology to physics, all still needed to be able to understand why cells do what they do, how physiological states are maintained and how pathological states emerge.