My holidays are spent with the nose into papers and the hands on the computer keyboard, working on quinquennial report. But I am back to my family in Italy, specifically in Sanremo, city of flowers, city of music, as it used to be the largest flower market and an important production center of flowers, and it hosts the most followed music festival in Italy. It is then not that surprising to walk in the streets and listen to music in the festive periods and in summer. Today, I got a break from work and went with my family to the main piazza of the town, where a group was singing various songs that contested the Sanremo Festival in the past.
The time came for “Signor Tenente” by Giorgio Faletti (1994), a song that was acclaimed by the critic and arrived second in the competition. A song that is musically flat, with a simple lyric, spoken rather than sung. A song that I had forgotten, but that is linked to an event I will never forget and changed me and many others in Italy, even very far from where it had happened.
In 1992, the prosecutor Giovanni Falcone was killed together with his wife Francesca Morvillo and three police officers in his security detail, Rocco Dicillo, Antonio Montinaro and Vito Schifani, when ‘Cosa Nostra’ blasted a segment of a motorway to kill his most feared enemy. Two months later, his friend and colleague Paolo Borsellino was killed with five police officers, Agostino Catalano, Walter Cosina, Emanuela Loi , Vincenzo Li Muli and Claudio Traina, by a car bomb while visiting his mother. Sanremo is a sea away from Sicily but in that tragic year we all felt Sicilians, raged against organized crime, close to the prosecutors, judges and the police forces – left alone by a political system that was about to be decimated by corruption scandals and that was in disarray.
“Signor Tenente” narrates that period from the point of view of the police (specifically Carabinieri) who, poorly paid and often in danger, do their duty while bombs kill.
These events might be difficult to understand outside Italy, or perhaps by the generation after mine. However, I wished to share with you, my friends, the feeling of pride I felt when, after a rendition of “Signor Tenente” finished, the square burst in a heart-felt applause, the warmest of the evening.
This is just a reminder that, in any country, most people are honest and good. There is time to criticize any authority, but there is also time to simply just thank, the police forces, the prosecutors, the justice system, and the people that in Italy and anywhere in the world fight injustice at great personal danger.
We have to be optimistic and hoping in a prosperous future for everybody, particularly in this period of the year, but optimism on its own makes very little to avoid sliding towards avoidable catastrophes. We can hope no storm will hit our towns in 2020 and live a happy life. At the same time, we can speak about the possibility of storms landing on our homes campaigning for strengthening river banks, coastal protections and flood barriers. Because our optimism should be well-spent in the hope that our actions will be successful rather than in the hope that our inaction will be rewarded by chance.
*** Lucio Dalla (1943-2012), a famous Italian singer, released a beautiful song in 1979, l’anno che verra’ (‘the year to come’). This song does not speak about war but of a troubled period of Italian history, when the country was shattered by political terrorism, when people’s worries were addressed by politicians by the constant renewals of promises of a prosperous future. This iconic Italian song is not just wonderfully and sadly contemporary, but also deeply meaningful outside Italy. It is thus a pity that, to my knowledge, no English rendition was ever attempted but scroll down to the bottom of this blog-post for a translation and the link to the song.
*** By many, l’anno che verra’ is considered an anti-war song. And being in Italy speaking with life-long friends, it came back to me. Yes, because during the last year, something has happened. Since the financial crisis, some of us has spoken about worries, at least privately, for an international political context similar to the period that preceded world-wide wars. Until recently, most people would respond to these concerns as if they were related to an abstract possibility, a distant scenario. Lately, I started to notice reactions that are more emotionally involved. Some people respond with an explicit wish for authoritarian figures that could bring back order and prosperity to people. Many others, quietly share their concerns, as to liberate themselves from an untold secret, something they never liked to speak about worried to be judged. Then I find myself speaking about the possibility of war with people from different countries and backgrounds, a discussion that is rarely met with skepticism by now. People does not appear pessimistic, desperately looking into the barrel of a gun, but realistically discussing about something that can happen and they wished to avoid.
*** Some politicians are promising us a prosperous future. At the same time, they are playing a complex chess game in an international scenario where the global geopolitical structure is in slow and constant flux, towards a new balance we cannot predict. Some politicians promise a wonderful year ahead, but advocate policies that lead to friction and conflict with other countries, a scenario that rarely leads to a peaceful and prosperous life. While I think that mainstream media could do a better job explaining to us what is happening, even just to reassure us, or to keep us alerted about the storms forming at the horizon, I feel I can do just one thing for now.
*** With a mild optimism that people will reject conflict and embrace international cooperation, with adequate scrutiny on the actions of their politicians, I dedicate to all of you Lucio Dalla’s song. Because there is still time to reinforce the river banks, the flood barriers and the coastal protections that defend our democracies, human and civil rights from the storms ahead.
Dear friend, …
The year to come by Lucio Dalla
Dear friend, I am writing to you so that I can distract myself a bit And since you are very far away, I will write to you with that much more force. Since you left, there’s been great change The old year is over by now But something still isn’t right here. People don’t go out much at night, even when there are parties And there are individuals who have put sacks of sand next to the window And some are without words for entire weeks And for others, they have nothing to say Of the time that remains. But the television said that the new year Will bring a transformation And we are all already in expectation There will be 3 Christmases and people will celebrate all day Every Christ will descend from the cross Even the birds will make their return. There will be enough to eat and it will be bright for the entire year Even the mute will be able to speak While the voiceless already do so. And people will make love every day as they please Even the priests will be able to marry But only at a certain age And without a lot of pain will someone pass away, They will be perhaps the people who are too clever And those who are too foolish in each era. Consider, dear friend, what I write and say to you And how content I am To be here in this moment Consider, consider, consider, consider, Consider, dear friend, what one must make up In order to be able to laugh through it all In order to continue to hope. And if this year were to pass then in an instant Consider, dear friend How important it becomes That in this instant you be by near me again The year that is coming will pass after another year I am preparing myself and this is the news
Caro amico ti scrivo così mi distraggo un po’ e siccome sei molto lontano più forte ti scriverò da quando sei partito c’è una grossa novità l’anno vecchio è finito ormai ma qualcosa ancora qui non va. Si esce poco la sera compreso quando è festa e c’è chi ha messo dei sacchi di sabbia vicino alla finestra e si sta senza parlare per intere settimane e a quelli che hanno niente da dire del tempo ne rimane. Ma la televisione ha detto che il nuovo anno porterà una trasformazione e tutti quanti stiamo già aspettando sarà tre volte Natale e festa tutto il giorno ogni Cristo scenderà dalla croce anche gli uccelli faranno ritorno. Ci sarà da mangiare e luce tutto l’anno anche i muti potranno parlare mentre i sordi già lo fanno. E si farà l’amore ognuno come gli va anche i preti potranno sposarsi ma soltanto a una certa età e senza grandi disturbi qualcuno sparirà saranno forse i troppo furbi e i cretini di ogni età. Vedi caro amico cosa ti scrivo e ti dico e come sono contento di essere qui in questo momento vedi, vedi, vedi, vedi vedi caro amico cosa si deve inventare per poterci ridere sopra per continuare a sperare. E se quest’anno poi passasse in un istante vedi amico mio come diventa importante che in questo istante ci sia anch’io. L’anno che sta arrivando tra un anno passerà io mi sto preparando è questa la novità
NOTE: This assay is the introduction to my research vision I wrote five years ago but that did not make into the programme grant we wrote. I think this is still current and, as it is unlikely I will publish this text, I am releasing it in the public domain with very little editing. I should note, particularly, that some paragraph remains unreferenced.
The genome, biochemical networks and phenotypes |Somatic mutations and gene copy number variations (CNVs) accumulate over time, stochastically altering the abundance and the functions of gene products. At first glance, efforts to identify and characterize somatic mutations provided a comparatively simple model: a few hundreds genes (proto-oncogenes and tumour suppressor genes) are often mutated contributing mechanistically to tumourigenesis (driver mutations). Some driver mutations are very frequent, but driver mutations that are less frequent in a cancer type overall dominate in number within an individual tumour, presumably conferring a more subtle growth advantage than others taken individually [1,2]. Also CNVs are very common in cancer. Sometimes, a clear role of CNVs in tumourigenesis can be established; most of the times, however, the effects of CNVs are difficult to predict or characterize because of the very different possible dependencies between phenotype and concentration of a gene products (e.g., haplo-insufficiency, quasi-sufficiency, triplo-sufficiency, etc.) [3,4]. Concentration effects and “subtle driver mutations” complicate the interpretation of genomic studies and may be best described by a continuum model for tumorogenesis where the all-or-none effects of individual genomic alterations are the frequent exception rather than the rule [4,5]. Notwithstanding the invaluable insights that genomics studies have provided and will continue to provide in our understanding of cancer, diagnostics and therapy, the role of these genomic alterations in tumorogenesis will be better understood in the context of the alteration of molecular networks underlying the respective cancer-associated phenotypes [1,6].
Few phenotypes are selected by mutation, those that enable cancer evolution  by increasing clonal heterogeneity (by genetic mutation, aneuploidy or epigenetic instability) and that permit growing in a hostile environment (avoidance of immunosurvaillance, metabolic deregulation and stromal hijacking). Moreover, cell survival, cell fate determination and, later in cancer evolution, cell migration are the key phenotypes that make of cancer the devastating diseases it is. Genomic alterations select for these phenotypes by influencing a comparatively small number of biochemical networks. Indeed, cancer-associated somatic mutations cluster in pathways controlling cell-cycle or cell-death, RAS/PI3K/MAPK, TGFβ, APC, STAT, NOTCH, WNT, HH and mTor [1,4,7]. Unsurprisingly, somatic evolution of cancer reshapes a comparatively small number of biochemical pathways that control cellular and tissue homeostasis to offset, often in a subtle manner, the net proliferative rate of cells. The study of these pathways is no less daunting than the understanding of complex genomic alterations. However, biochemical networks have evolved to exhibit robustness in the presence of intrinsic noise present in biological systems (e.g. stochastic variations in transcription or cytokines concentrations). Robustness of biochemical pathways permit to stably encode for cellular functions and cellular states. It is therefore conceivable that the myriads of possible genomic alterations and individual gene-products simply concur to generate a discrete set of biochemical states corresponding to cancer-associated phenotypes.
Other “big data disciplines” (e.g., transcriptomics, proteomics and metabolomics) have provided the opportunity to study the working mechanisms of biological systems alongside genomics. Some groups have suggested that integrative biology [8,9], the effort to integrate data from these various disciplines, may permit avoiding the biases and inherent flows of individual –omics techniques and, at the same time, may deliver a new approach to the study of human disease. This approach is summarized by the term “network medicine” highlighting that molecular networks altered in disease can be both the target for future therapeutic strategies and the possible source of novel biomarkers. A biochemical network, common to many different cell types or even species, exhibit a different “network utilization” in different physiological and pathological contexts. Mutations can therefore offset the utilization of molecular networks and their dynamics. On the one hand, better understanding of how networks encode functional states and cellular decisions under physiological conditions and how these are altered in disease will offer more and better targeted therapeutic opportunities. On the other hand, defining cancer-associated network utilizations and engineering tools (probes and instrumentation) to reveal them will provide fundamental insights to optimize patient stratification for improved theranostics and prognostics.
Heterogeneity, causality and phenotypes | Phenotypic heterogeneity, including genetic and epigenetic polymorphism, and polyphenism, is at the basis of both unicellular and complex lifeforms. These three levels of phenotypic heterogeneity are recapitulated in cancer and constitute often insurmountable obstacles to effective therapeutic intervention. Intra-tumour heterogeneity, either within the primary tumour, within a metastasis or between different metastatic foci is indeed the primary cause for the emergence of drug resistance and tumour relapse. The genetic basis for phenotypic heterogeneity within a tumour is rather established. However, other non-genetic factors can be regarded as equally important.
For instance, upon treatment, a fraction of tumour often exhibit drug resistance. In part, this can be caused by pre-existing tumour cell clones carrying mutations that, by chance, will confer resistance to any given drug. Alternatively, this may be caused by tumour initiating cancer cells, stem-like cells that are usually quiescent, less vulnerable to treatment and that can regenerate the tumour upon termination of the therapy. Moreover, non-Darwinian mechanisms for the emergence of drug resistance have been proposed as well, whereby cells trigger a transient drug-resistant phenotype that, in time, can be then converted to a stable inheritable state by subsequent somatic evolution. Fractional killing may also be explained by non-genetic heterogeneity. For instance, Spencer et al. have shown that in a clonal population of cells, TRAIL elicits a heterogeneous phenotypic response with cells undergoing apoptosis at different times or surviving indefinitely. The authors elegantly demonstrate that this phenomenon is caused by stochastic variations in the abundances of the many proteins involved in the apoptotic molecular network.
Genomics, transcriptomics, proteomics and metabolomics allow the characterization of tens of thousands of biomolecules at the same time. Furthermore, the increasing sensitivity of these techniques provides – or may provide in the future – single cell “–omics” characterization. However, the invasiveness of these techniques will limit their applications to the study of individual time points. Thus, causality can be established only by inference. Techniques capable to provide low invasiveness and biochemical information on living cells are thus extremely useful to complement models derived by ‑omics techniques and to provide a tool for testing hypothesis derived from analysis of big data.
It is thus evident that time-lapse imaging of individual living cells with biochemical information is strategic for the understanding of the heterogeneous response of biological systems and to establish causality between biochemical events and cellular decisions. At the same time, genetic heterogeneity within a tumour and between tumours induces differences in network utilizations with significant consequences for prognosis and treatment. Also in this context, biochemical imaging techniques are necessary to understand the phenotypic heterogeneity of a tumour and, at the same time, may be useful to define network-based biomarkers.
The next generation of Systems Biology | Several groups have identified the need to integrate fluorescence microscopy in the systems level study of the cell and organisms [10-15]. The term “Systems Microscopy” has been suggested for the description of microscopy tools applied to this field . In order to strategically complement other approaches, Systems Microscopy has to deliver single cell resolution, temporal characterization of living cells and high quality quantitative data and has to be applied to the most appropriate biological context (e.g., for epithelial cancers, adherent 2D, 3D, organotypic cultures or in vivo rather than in suspension or cellular homogenates) . Whereas –omic techniques can sample the biological space over the fullness of biochemical moieties (genes, RNAs, proteins, metabolites) albeit with poor sampling of individual cellular behaviours and spatio-temporal organization, Systems Microscopy samples the fullness of the spatio-temporal organization of molecular networks but reports about a limited number of gene products or biochemical events . Therefore, Systems Microscopy elegantly complements big data studies.
We envisage two (not mutually exclusive) approaches to Systems Microscopy: high throughput screening platforms and single cell biochemical multiplexing. High Content Screening (HCS, also known as imaging cytometry or high throughput imaging) is the current tool of choice for Systems Microscopy. Relying on robotics, automation and unsupervised or semi-supervised data analysis, HCS enables the screening of large numbers of cellular perturbations (e.g., siRNA or compound libraries) with commercial instrumentation making the correlation of these perturbations with morphological estimators and fluorescent markers possible. Several groups have also highlighted the importance of integrating quantitative biophysical imaging techniques such as Fluorescence Correlation Spectroscopy (FCS) and Foerster Resonance Energy Transfer (FRET) in Systems Microscopy in order to deliver data of high quality. Despite this, HCS has been integrated with these techniques only in a few academic-based efforts [16-19]. HCS expands the sampling of biological space of imaging technologies to deliver another set of “big data” but with single cell resolution.
We are pursuing a different approach to Systems Microscopy that maps in space and time an increasing number of fluorescent markers within the living cell. Fluorescence is not amenable to the simultaneous detection of many fluorescent molecules because of the broad excitation and emission spectra of common fluorophores. Therefore, we are determined to develop new techniques (bioprobes and instruments) that exploit all properties of light (photon arrival times, colour and polarization) efficiently to maximize the biochemical resolving power of microscopy. We aim to monitor nodes of molecular networks (e.g., quantifying the dynamic phosphorylation of several substrates) in living cells in response to stimuli, discerning between physiological and pathological (oncogene-driven) network behaviour (topology). The integration of Optogenetics tools (e.g., light-inducible oncogenic signalling) enables perturbational analysis of biochemical networks and facilitates the execution of complex biochemical imaging assays fully automated with no requirement for sample manipulation other than by light. Therefore, these techniques will be strategic for the study of biological networks at low throughput with high quality data; thanks to this all-optical approach, they may also be integrated with HCS increasing the quality and quantity of information and decreasing steps in chemical manipulations of the samples (e.g., addition of doxycycline to stimulate the expression of a gene)
 Vogelstein et al. (2013) “Cancer Genome Landscapes” Science  Wood et al. (2007) “The genomic landscapes of human breast and colorectal cancers” Science  Solimini et al. (2012) “Recurrent hemizygous deletions in cancers may optimize proliferative potential.” Science  Davoli et al. (2013) “Cumulative Haploinsufficiency and Triplosensitivity Drive Aneuploidy Patterns to Shape the Cancer Genome” Cell  Berger et al. (2011) “A continuum model for tumour suppression.” Nature  Jorgensen&Linding (2010) “Simplistic pathways or complex networks?” Current Opinions in Genetics and Development  Hanahan&Weinberg (2010) “Hallmarks of Cancer: The Next Generation” Cell  Erler&Linding (2010) “Network-based drug and biomarkers” J Pathology  Barbasi et al (2011) “Network Medicine: A Network-based Approach to Human Disease” Nat Rev Genet  Megason et al. (2007) “Imaging in Systems Biology” Cell  Verveer&Bastiaens (2008) “Quantitative microscopy and systems biology” Histochem Cell Biol  Ankers et al. (2008) “Spatio-temporal protein dynamics in single living cells” Current Opinion in Biotechnology  Pepperkok&Ellenberg (2006) “High-throughput fluorescence microscopy for systems biology” NAt Rev Mol Cell Biol  Lock&Stromblad (2010) “Systems microscopy: An emerging strategy for the life sciences” Exp Cell Res  Conrad&Gerlich (2009) “Automated microscopy for high-content RNAi screening” J Cell Biol  Esposito et al. (2007) “Unsupervised fluorescence lifetime imaging microscopy for high content and high throughput screening” Mol Cell Proteomics  Mathews et al. (2008) “A high-content screening platform utilizing polarization anisotropy and FLIM microscopy” SPIE BIOS  Talbot et al. (2008) “High speed unsupervised fluorescence lifetime imaging confocal multiwell plate reader for high content analysis” J Biophotonics  Barber et al. (2013) “The Gray Institute ‘open’ high‐content, fluorescence lifetime microscopes” J Microsc
“I have worked hard for three years and now that I
believe I understand the mechanism, the funding is over”. “I am at
the third referee round in five different submissions and I am always getting
different requests”. “My grant was not funded because of insufficient
preliminary results”. “I do not understand why they got a promotion
and I am struggling to keep my job with a similar track record”. “I
worked days and nights and the panel dismissed me with meaningless
questions”. “My friend never recovered from a mental breakdown”.
“I have written the proposal for a month and it was rejected with one
sentence, on subjective grounds”. “The referees were very positive
but the panel was unimpressed”. “I did not get funding but those in
the panels did”. “I got bullied but a committee found that nothing
“Yes, I understand you. It is unfair but this is how
*** No… it is not me moaning but a collection of whispers, complaints and shouts you can hear in the corridors of Academia. Along with comforting words, the response to a colleague in a temporary moment of discomfort or a prolonged stage of distress are often two. One might be an explanation of what a colleague might have done objectively wrong or how to avoid typical traps in the various stages of academic assessment. The other is just the acknowledgement that at least in many, if not all, cases… well… this is how Academia works and we have to be resilient and keep going*. However, this post is not about complaining but more about the human factor often lost in Academia.
In the last few days, twice I heard or read appeals of ‘being king’ to people in the academic context. Once, in a speech by our Director, Prof. Ashok Venkitaraman, opening our retreat on Friday. His speech did mention academic excellence but it was particularly focused on people as described by our colleague Dr Ben Hall.
His words resonated with most of us as kindness is far too
often forgotten in Academia, probably because in very competitive environments,
people are supposed to be all so full of themselves and thick-skinned that
everything goes. In truth, like in any work environment, the large majority of
people treat each other with respect and just a few then spoil it for everyone
Just a day later on Saturday, in a private conversation completely unrelated, a friend pointed out that the Teichmann laboratory at the Wellcome Sanger Institute, adopted as a lab motto the words “Be bold. Be brilliant. Be kind.“
These two almost trivial observations (from our Director and another successful Academic) made me think. Why do we need to make such appeals for kindness? After two decades of living a life within Universities, my experience of the Academic environment is of a very tolerant, liberal and progressive environment. Of course, there are plenty of issues to be fixed, common to other sections of society, but the general attitude and ethos – in my experience** – was mostly positive. Then why do we eventually feel the need to appeal to kindness?
*** My opinion is that the obsession for ‘independent’ academic assessment and competition is in part selecting for certain characters. Being ruthless and selfish helps in any competitive environment, as it increases the likelihood to seize resources. However, I do not think this is just the issue. Most academic assessment is either performed anonymously or by panels that often have no knowledge of the person they have to judge. Various forms of peer-review (either for publishing or funding) are designed to be objective and independent. While peer-review is the best system I can also think of, its issue is that – eventually – it is not objective and it is not independent but in trying to be, it loses any human touch. Even when interviews are at the core of assessment, these are brief (5-20mins) and very focused, in any case preceded by anonymous reviews. The lack of human connection and two-way personal dialogue, I think, dehumanize the process of assessment and triggers ‘unkind’ behaviours. The problem, perhaps, we focus too much on projects and not enough on people.
I might be still naive, but in my opinion, the most important resource in any work environment, and also in Academia, is people. Recently, we prepared a leaflet for outreach with the motto “Our superpower is you”, meaning that science main resource is one: people. Unfortunately, the structure of academic assessment and a highly tapered career pyramid with huge turn-overs at its base, create rent-seeking behaviours and an environment that can be harsh in general, or at least in key moments of one’s career. We should think about people investing in people for the benefit of people, not just in projects.
I know that this is perhaps a tiny bit too idealistic and any type of assessment has flows. Probably, we cannot really solve this problem, maybe it is not a problem in itself. But I would like to leave you, my friend, with a provocation. I dare you not just being kind (if you read until here you might agree with the general concept) but challenge everyone that is not, be kind when you review a paper or a grant, particularly when you have strong criticisms to share. If you are an Editor, the head of a panel, academic or not, I dare you challenging unkind behaviour and disqualifying any critique that is not delivered with respect. I dare you all speaking publicly about the need to be excellent in science, but also in our humanity. Because if we wait longer for a top-down change, even though many at the top are wonderful people agreeing with the ‘be kind’ concept, we will keep losing our human capital. I dare you last, to use this or any other badge of your choice in your website or public communication. The large majority of people is good people, in any environment, we just need to remind everyone that it is not acceptable to be otherwise:
* to avoid misunderstandings, I should clarify that I might also respond in this way, it is not a criticism on trying to be helpful explaining how the system might work. ** VERY IMPORTANT TO ME, this is my own experience. I am fully aware of other very different experiences, and structural problems. Here I am speaking about a general attitude and – as I am committed in Equality Diversity and Inclusiveness in Academia, I am fully aware that there are plenty of problems to be solved. I do not want that this specific statement about Academia being generally a liberal and progressive environment (which is what I think) will be misunderstood as if Academia is perfect, indeed my post would suggest otherwise.
As an immigrant in the UK, it took some time to understand the deeper meaning of the remembrance day. In fact, remembrance day is lived by different British people in different ways, and to truly embrace this event, one has to stare a red poppy and feel what it means for them. You should have an intimate meaning for the red poppy to relate to the remembrance day. If you do, the wearing of the red poppy becomes not only a charitable gesture but a deeply meaningful action. As an immigrant from a country where the red poppy is not a tradition, therefore, it is only after one decade in the UK that I can finally embrace this day full-heartedly.
Remembrance day is approaching. I hope most people will reflect on what this day actually represents. It is the day where Commonwealth nations remember the soldiers fallen during the first world war and by extension, it is the day many intend to pay tribute to those who died in wars. In these weeks, many people will use war rhetoric and will revive patriotic emotions. Many people will proudly wear red poppies, to support veterans, as a statement of national pride, to remember the fallen soldiers, or for social pressure. Like every year, the news will invite comments, there will be vast support, but also critical opinions, and critical rebuttals of those critiques. Eventually, remembrance day ends up to be all those things. This year, however, I will have my first true remembrance day when I will not care about what this means for others, but I will care only about what it means for me. The reasons are two. One is that after many years I relate to British traditions as my own. The second is that my daughter, a British citizen who self-define as English, is in year 1 at school and I have to dialogue with her about the red poppy.
However, the colour of this flower and the origin of this symbol – the devastated fields where soldiers died during the first war and were then covered by red poppies – are so evocative that many people cannot refrain to associate the colour of the scarlet red poppy to the blood of soldiers who died in the war.
So, what is for me the red poppy? It is the blood of the soldiers shed during wars, but it is also the blood of the civilians crashed between opposing fronts. The bloody tears of those who survived, the broken families, the broken hearts, the children, the mothers and fathers, the elderly who died in battle or were visited by death at home. To me, the red poppy and remembrance day are reminders that we should always do anything possible to avoid conflict and war.
As war rhetoric came back fashionable also in democratic countries, when authoritarian movements are gaining the consensus of the public, and when too many people are proud to divide nations rather than to unite, we should not escape from the deeper meaning of this day.
To me, the red poppy is the blood that should never be spilt again but that will, and does.
And therefore, I will embrace this remembrance day as my own. With gratitude for brave soldiers that defended our freedoms but with shame because we have asked and we will ask them again to kill and to die instead of just being vigil, watching with pride our democracies working peacefully together.
In fluorescence microscopy, colocalization is the spatial correlation between two different fluorescent labels. Often, we tag two proteins in a cell with distinct fluorescent labels, and we look if and where the staining localizes. When there is a “significant overlap” between the two signals we say that the two molecules “colocalize” and we might use this observation as possible evidence for a “functional association”. We might argue that measuring colocalization in microscopy is one of the simplest quantitation we can do. Yet, many horror stories surround colocalization measurements. This post is not a review of how to do colocalization, but a brief casual discussion about a few common controversies that is – as often I do – aimed to junior scientists.
“I am imaging GFP, but the image is blue, can you help me?”. Well, this is not a question related to colocalization but it illustrates a fundamental issue. In truth, cell biology is such an inherent multidisciplinary science that – in most cases – a researcher might require the use of tens of different techniques on a weekly basis. It is thus not surprising that many researchers (I dare say most) will be an expert on some of the techniques they use but not all. Microscopy is particularly tricky. To be a true expert, you need to handle a feast of physical, engineering and mathematical knowledge alongside experimental techniques that might span chemistry, cell culture and genetic engineering. However, the wonderful commercial systems we have available permit us to get a pretty picture of a cell with just a click of a button. Here the tricky bit, you want to study a cell, you get a picture of a cell. One is lead to confusing the quantity that intends to measure with the information that is actually gathering and with its representation. This is true for any analytical technique but as ‘seeing is believing’, imaging might misrepresent scientific truth in very convincing ways. Hence, with no doubts that upon reflection the non-expert user would have understood why the picture on the screen was ‘blue’, the initial temptation was to believe the picture.
Question what you set out to measure, what the assay you have setup is actually measuring and what the representation is showing. Trivial? Not really. It is an exercise we explicitly do in my lab when we have difficulties to interpret data.
“It is yellow, they colocalize, right?”. Weeeeeeeeellll… may be, may be not. Most of you will be familiar with this case. Often researchers acquire two images of the same sample, the pictures of two fluorescent labels, one then is represented in green and the other in red. With an overlay of the red and green channels, pixels that are bright in both colours will appear yellow. I would not say that this approach is inherently flawed but we can certainly state that it is misused most of the times and, therefore, I try to discourage its use. One issue is that colour-blindness, not as rare as people think, renders this representation impractical for many colleagues (so my colour highlights!), but even people with perfect vision will see colours with lower contrast than grey-scale representations, and green more than red. Eventually, to ‘see yellow’ is almost unavoidable to boost the brightness of the underlying two colours to make the colocalization signal visible. This can be done either during the acquisition of the image often saturating the signal (bad, saturated pixels carry very little and often misleading information) or during post-processing (not necessarily bad, if declared and properly done). Either way, at the point you are doing this, your goal to be quantitative has been probably missed. The truth is that a lot of biological work is non-quantitative but faux-quantitative representations or statistics are demanded by the broader community even when unnecessary. Let’s consider one example with one of the stains being tubulin and the other a protein of interest (PoI). Let’s assume the PoI is localizing at nicely distinguishable microtubules in a few independent experiments. Once the specificity of the stain is confirmed, the PoI can be considered localized at the microtubules (within the limitations of the assay performed) without the need for statistics or overlays. Unfortunately, it is not very rare to see papers, also after peer-review, to show diffuse stainings of at least one of the PoI and perhaps a more localised stain of the second PoI and a ‘yellow’ signal emerging from an overlay is considered colocalization, instead of what it is: just noise. Another common issue is localization in vesicles. Again, any cytoplasmic PoI would appear to colocalize with most organelles and structures within the cytoplasm with diffraction-limited techniques. Sometimes punctuated stainings might partially overlap with known properly marked vesicles, let’s say lysosomes, but not all. Then the issue is to prove that, at least, the overlap is not random and, therefore, statistics in the form of correlation coefficients are necessary.
“The two proteins do not colocalise, two molecules cannot occupy the same volume” Really!? Well, from a quantum mechanics standpoint…. No, do not worry, I am not going there. I have received that criticism during peer-review in the past and until recently I thought this was a one-off case. However, I have recently realised that I was not the only person reading that statement. I am really uncertain why a colleague would feel the need to make such an obvious statement except for that condescending one-third of the community. I should clarify that to my knowledge no one implies physical impossibilities with the term colocalization. That statement is perfectly ok in a casual discussion or to make a point to teach beginners the basics. Some of us also might enjoy discussing definitions, philosophical aspects related to science, controversial (real or perceived) aspects of techniques, but better at a conference or in front of a beer, rather than during peer-review. The issue here is that while it is reasonable to criticise certain sloppy and not too uncommon colocalization studies, in general colocalization can be informative when properly done.
“So, is measuring colocalization useful?” Homework. Replace ‘colocalization’ with your preferred technique. Done? Now try to make the same positive effort for colocalization. Every technique is useful when used properly.
You might have noticed I marked some words in my introduction: colocalize, significant overlap and functional association. It is important we understand what we mean with those words. Colocalization means co-occurrence at the same structure, a non-trivial correlation between the localization of two molecules of interest, within the limits defined by the resolution of the instrumentation. The “significant overlap” should be really replaced by “non-trivial correlation”. Non-trivial, as diffuse stainings, unspecific stainings, saturated images can very easily result in meaningless colocalization of the signals but not of the molecules of interest. Correlation, as the concept of overlap might be improper in certain assays, for instance in some studies based on super-resolution microscopy. After we did everything properly, we still cannot say that if protein A and protein B colocalize they interact (see slide). However, we can use colocalization to disprove the direct interaction of two proteins (if they are not in the same place, they do not interact) and we can use high-quality colocalization data to suggest a possiblefunctional association that might be not a direct interaction, and that should be then proven with additional functional assays.
Then, my friends, do make good use of colocalization as one of the many tools you have in your laboratory toolbox but beware that just because it is simple to acquire two colourful pretty pictures, there are many common errors that people do when acquire, analyse and interpret colocalization data.
P.S.: if I cited your question or statement, please do not take it personally. As I have written, not everyone can be an expert of everything and the discussion between experts and non-experts is very useful, so making real-life anonymous examples.
Well, we are not rocket scientists but we could not miss the opportunity to speak about the space race at the Science Day of our local Primary School so close to the 50th anniversary of the moon landing. The inspiration came from the book “Space Race” by Deborah Cadbury. After reading it, a summary of the space race became one of the bedtime stories we tell our daughter. When the time came to pick a story to tell at the Science Day, after discussing work-related topics ranging from DNA extraction to optics, we opted for the space race and the moon landing. We are no experts in outreach but after a few years of volunteering, we can tell you that a well-done job is a hard job and a rewarding one. Also, like for any other communication-based activity, the three main tricks to reach impact are i) tell a compelling story ii) think about your audience and iii) be prepared.
The space race and the moon landing can be still very inspirational story to tell. It is a story of exploration, science and technology, it is a race but also a monumental teamwork. It has its roots in the cold war and the manufacturing of weapons of mass destruction… a story that ended up with a blast-off to the moon to inspire generations instead.
The first step in the organization for us was to see which are the basic experiments people do in the classrooms around the world. We clocked several hours over a few weeks trying to understand what is possible and what might excite pupils. Google and YouTube were the most obvious starting point. This activity was fun (well, particularly if you are a bit geeky!) but also stressful when we noticed we were not converging to a particular set of experiments we wished to demonstrate. Everything changed when we decided which story we would tell, as we were able to rethink all the material we explored from a different perspective.
The second step was gathering materials and more information. We studied facts about the moon, rockets and the space race. Most of it was general information that could have been useful to answer questions, some of it ended up in an introduction supported by a few slides. At the same time, we went shopping both targetting specific items but also browsing toy shops randomly trying to identify anything that could be useful. We kept brainstorming about a possible story-line and experiments to demonstrate, finally converging to a plan.
The third step was to prepare the day. We prepared a few slides and selected a few fun facts to share. While unnecessary strictly speaking, in private we discussed sensitive topics, the drive of science and technology during the cold war to prepare weapons of mass destruction, how this turned to a different type of race to reach the moon, with elements of competition and team working. While, of course, we did not discuss these topics in the classroom, eventually we were able to emphasize concepts that are important to us, the use of science and technology for good purposes (exploration and discovery) rather than bad ones (war), racing as a fun activity but highlight how teamwork is essential to reach very high goals.
Before the day came, we just needed to be sure that the day at school was organized properly, and we were lucky that Emily Boyce from the Babraham Institute had organized an excellent schedule for the entire day, logistics and liaised with teachers, so we could spend all the time we could just on the activities. Finally, risk assessments. Yes, they are boring and sometimes they seem superfluous but if done properly they help you think about what could go wrong and avoid accidents to happen. As they are anyway a legal requirement, make best use of them to help you planning the event logistics.
On the day
We had prepared a few slides with full screen images from the Apollo mission (a fired-up Saturn V, the moon lander, Armstrong’s footprint, a map of the solar system) and we ad a passionate and engaging chat with the students (see ‘Let’s talk about the Moon’ section). While the students were engaged, one of us set up all the contraptions needed for the latter part of the session.
Next, we wanted to introduce the concept of propulsion and Newton’s third law of motion. We started with this toy we found in a store:
We just showed how air pushed to the ‘rocket’ can lift it up, just small jumps catching the rocket with the hands. With the reception class, we let some children playing with it, while with year 3, we did some jokes (e.g., ‘do you see a big man or woman pushing a large pedal under the rocket?’ while pointing to the image of a fired-up Saturn V ready for lift-off) and we asked to explain to us what was happening.
Next, we told that this is not how rockets work and release rocket balloons in the room that we had inflated before entering the room and clipped. When thrown (not just released them speedless), these balloons are propelled around the room.
We engaged the students asking what they thought it was happening and clarified that air is getting out of the balloon and pushing the ballon ahead. The uncoordinated movement of the rocket balloons let us introduce the next contraption. We had placed a mock-up moon in the corner of the classroom. Because of the limited time available we prepared it at home with recycled materials within a plastic bag forced into a spherical shape with cello tape then covered with aluminium foil. We left that knotted handles of the bag out of the aluminium foil to anchor two fishing lines. The fishing lines were several meters long to cover the length of a classroom. There are plenty of instructions over the internet on how to build a rocket balloon guided by a string. I would recommend a more visible line than the one I found in the local shops but here the materials we used.
We inflated the balloon with an air pump, pasted the straw on the top of the balloon with two long pieces of cello tape and we drew a fun face on the balloon with a permanent marker. We then took one of the prepared fishing lines and demonstrated how the rocket balloon could reach the moon, asking the children to do a countdown after which we released the clip. This was just an introduction to the main activity of the session where we split the class into groups and gave materials to prepare and decorate their own balloons. As we pre-made two fishing lines, we let them race in pairs of groups to the moon.
We had planned to stop here if we ran out of time but prepared also a different ending. Our sessions were 45 minutes long and we discovered there was enough time for it. We pointed out there is no one inflating rockets and we introduced the concept of rocket fuel.
Before the beginning of the session, we poured two shots of malt vinegar in a tall glass. When the time came, we uncovered the glass and chatted about liquid and solid fuels, introducing the concept of chemical reactions used to propel a rocket. We then added a teaspoon of bicarbonate of soda to show the formation of large amounts of froth. During testing at home with the materials we could find in the local shops, we accidentally realize that malt vinegar would generate a lot of froth and that we could use this as a trick for comparing the froth to the vapours and flames coming out of a rocket engine.
Finally, we showed how this could be used to propel a rocked by inflating a balloon. We tested a few materials and opted to use a small plastic bottle with white vinegar. Keep in mind we used what we could find at the local shop and other combinations could work better. We added four shots of vinegar into the empty juice bottles. The labels were removed and we wrote the content with a marker. We also always had the bottle under control, but the obvious shape of the bottle attracted attention from younger children and we probably would use a different bottle or covered it with paper if we were to redo it, just to avoid a child grabbing it and trying to drink from during the confusion of some of the activities.
To make things simple on the day, we prepared balloons filled with two teaspoons of bicarbonate of soda, gently clipped, with excess powder blown away from the opening of the balloon.
At the right moment, we removed the clip and attached the balloon to the neck of the bottle paying attention to not let any powder drop into the bottle. Then we raised the balloon permitting the powder to mix with vinegar while holding the neck of the bottle firmly with the hands to avoid the balloon shooting in the class and spraying vinegar. We kept the vinegar a bit warmer than room temperature by pouring some hot water in a cup and keeping the bottle of vinegar in it. This was done in a staff room for safety. The lukewarm vinegar reacts faster with baking soda resulting in very fast inflation of the ballon.
This is how we prepared our Science Day activities. Each of the experiments is rather common and we got inspired by a lot of materials we read and watched. However, it is important to test every single experiment at home, identify the most appropriate materials and doses in order to ensure the timely and safe execution of each of them. Together, we probably invested about 50 hours of work in this activity in addition to the day spent at the school, spending evenings and spare time to plan the activities.
1) Who can tell what the Moon is? It is a space rock we call a satellite that turns around (orbits) the Earth. It was formed about 4.5 billion years ago when a large space object hit the Earth, and the debris from this crash formed the Moon. The Moon completes its turn around the Earth in 27.3 days.
2) What colour is the Moon and what it is made of? It’s made of mostly dust and rocks, there is no atmosphere, no water and no life. Just mountains and large craters. The Moon itself does not produce any light; we see it shining because the Moon reflects light from the Sun.
3) We see only one side of the Moon (also called near side), why is that? While orbiting around the Earth, Moon also rotates around its axis, and this rotation takes the same amount of time as it does to complete the turn around Earth. That’s why we can only see only one side of the Moon (about 60% of its surface).
4) What is the temperature at the Moon? Hot or cold? Well, both actually. During the day when the Sun hits the surface of the Moon temperatures can reach 127°C. You can fry an egg without a stove. During the night, the temperature can go down to freezing -173°C.
5) Did you know that you weigh six times less on Moon? That’s because the gravity (the force that pulls us down to the ground) on the Moon is weaker than the gravity on Earth. You can jump really high on the Moon. In fact, astronauts have to wear their heavy boots to keep them down on the ground.
6) How far is the Moon from us? It is really really far, about 384.000 km. If you are to drive this distance by car it would take you about 150 days. However, thanks to the rockets built by very talented teams of engineers and scientists we can reach the Moon in just 3 days, and we have exciting opportunities to explore the space!
We humans have always been curious about the world around us. The Moon was always one of our biggest curiosity. Using his telescope, Galileo have documented many observations about the Moon in 1600s. We have come a long way since then and thanks to the space rockets we have built we can explore places “Where No Man Has Gone Before”
Rockets were initially developed for wars, unfortunately. Luckily, later on, we realised we could use and develop rockets for much better goals – to explore the deep space. The Space race began. The first country to send an astronaut into Space was the Soviet Union, with Yuri Gagarin and his Vostok 1 capsule. The Soviet Union, sent also the first satellite in orbit (Sputnik) and the first rocket to the Moon, with the spacecraft Luna 1 passing very near to the Moon and Luna 2 crash-landing on our Satellite in 1959. It was then in 1969, that an incredible adventure lead by USA brought the first people on the Moon. Engineers and scientist in USA built a massive rocket, Saturn V and brought three brave astronauts up to the Moon with their Apollo 11 mission. The team was led by Neil Armstrong who made the first step on the Moon. As Neil Armstrong said, this was “ one small step for [a] man, one giant leap for mankind!”
Armstrong’s footstep will be a long lasting one as well. It will last in our culture, as the most exciting moment of a long adventure. It will last a long time on the Moon, where there is no wind to wipe it off.
Is it a cat? Is it a dog? Is the average between a cat and a dog a real thing, perhaps a caog or a doat?
Not all science should be based on single cell detection, and there are plenty of cases where single cell measurements are superfluous. However, too often we fail to appreciate the huge mistakes we can do in biology when we forget the assumptions we do when using population measurements.
But which assumptions do we really do?
Often implicitly, when doing population measurements (e.g., Western blots, sequencing, proteomics, etc…) we assume that populations of cells we measure are homogeneous and synchronous. Or at least we assume that these differences are unimportant and that they can be averaged out. In the best cases, we try to enforce a degree of synchronicity and homogeneity, experimentally. In reality, one of the most important assumptions we implicitly do is that the system we analyse is an ergodic system. In physics and statistics, an ergodic system is a system that, given a sufficiently long time, explore all its possible states. It is also a system where – if sufficiently sampled – all its states are explored and, consequently, averages over time on a single cell and averages over a population at a given time are the same. However, there are limits to this assumption in biology. The obvious example is the cell cycle. There is significant literature about ergodicity and cell cycle [e.g., 1, 2, 3] and how this principle can be exploited, but…
The lottery for cell division makes you grow faster.
There is a particular phenomenon that we encountered while we were working on this project  that fascinated me for its simplicity and consequences. How cells can increase their fitness (i.e. their growth rate)? One obvious answer is by dividing faster. Another, at first glance less obvious answer, is by exhibiting an heterogeneous cell cycle length. Let’s consider a population of cells that divides every 24 hours. Over one week, these cells will have 128 times the original population size. Now, let’s consider cells that divide on average every 24 hours but exhibit variation in cell cycle length, randomly, with a standard deviation of 4 hours and a normal distribution. Cells with 20 hours or 28 hours long cell cycle are equally probable to occur. However, in one week, cells with a 28 hours long cell cycle length will grow 64 times and cells with a 20 hours long cell cycle length will grow about 380 times. On average, these cells will grow ~200 times, that is much faster than cells dividing precisely every 24 hours (128 times). This is true for any pair drawn at equal distance from the two sides of the average; these pairs are equiprobable, thus cells dividing at a given average cell cycle length grow faster at increasing heterogeneity. Let’s remember that this can occur not just in the presence of genetic differences, but even just for stochastic variations where the progeny of one cell will not keep the same cell cycle length but will keep randomly changing according to an underlying distribution. This is a phenomenon that has been observed experimentally, for instance, in yeast  with single-cell measurements but that is occurring in any cellular systems as described in  and our own work . Population measurements might conceal these very important phenotypic or mechanistic differences.
The sum of two normal distributions is not another normal distribution.
The beauty of the normal distribution is that it is such a ‘well behaved’ distribution and, at the same time, it represents many physical and biological phenomena. If a population we are characterizing is made of two normal distributions, their average is the average of the normal distribution. If these have the same average, the variance of the sum will be the sum of the variances. These basic and useful mathematical relationships can be also rather misleading. In fact, while these statements are mathematically correct, two populations of cells that ‘behave rather differently’, for instance in response to a drug, cannot be averaged. For instance, one cell population might be killed with a given concentration of a drug. Another population might be resistant. By detecting 50% cell death, we could assume – incorrectly – that dosing at higher concentrations we could kill more cells.
The plot shown below illustrates this basic principle. The blue and red distributions, averaged together, exhibit the same variance and average of the yellow distribution but they represent very different systems. If the blue distribution represents the sizes of cats and the red distribution the sizes of dogs, the yellow distribution does not represent the size distribution of any real animals. In other words, the average phenotype is not a real phenotype and, in the best case scenario, when there is a dominant population, it represents the most frequent (the mode) phenotype. In all other cases, where the homogeneity of the phenotype is not checked, the average phenotype might be simply wrong.
This is a very simple illustration of a problem we frequently encounter in biology, trusting our population measurements (averages and standard deviations over experimental repeats) without being sure of the distributions underlying our measurements. In the figure above, the purple distribution is a distribution where the average is the correct average of the blue and red distribution, but the purple distribution is the statistical error of the assay and it is unrelated to the scatter of the biological phenomenon we are measuring. Sometimes, we cannot do anything to address this problem experimentally because of the limitations of technologies but it is very important – at least – to be aware of these issues.
Just for the most curious, I should clarify that for two Gaussian distributions with relative weights A and B, we can define a mixing parameter p=A/(A+B). The average of the mixed population will be simply μP=p*μA+(1-p)*μB, i.e. for p=0.5 is the average of the means. The apparent variance is σP^2 = p*σA^2+(1-p)*σB^2+p(1-p)*(μA-μB)^2, i.e. σP^2 is the average of the variances summed to the squared separation of the two averages weighed by the geometrical averages of the mixing parameters of the two populations.
Collective behaviour of cells is not an average behaviour, quite the contrary.
When discussing these issues, I am often confronted with the statement that we eventually do not care about the behaviour of individual cells but with the collective behaviour of groups of cells. There are two important implications to discuss. First of all, when arguing the importance of single-cell measurements, we do not argue the importance of studying individual cells in isolation. Quite the contrary, we should measure individual cells in model systems the closest to the physiological state. However, many assays are incompatible with the study of cell behaviour within humans and we resort to a number of model systems: individual cells separated from each other, 2D and 3D cultures, ex and in vivo assays. The two arguments (single cell measurements or measurements in more physiological model systems of tissues or organisms) are not the same.
Second, collective behaviours are not ‘average behaviours’. There are great examples in the literature but I would advise just even to visit the websites of two laboratories that I personally admire. They nicely and visually illustrate this point, John Albeck’s laboratory at UC Davis and Kazuhiro Aoki’s laboratory at NIBB. Collective behaviours emerge from the interaction of cells in space and time as illustrated by waves of signalling or metabolic activities caused by cell-to-cell communication in response to stimuli. The complex behaviours that interacting cells exhibit, even just in 2D cultures, can be understood when single cells and their biochemistry are visualized individually. Once again, phenotypes or their mechanism might be concealed or misinterpreted by population or snapshot measurements.
This is, of course, not always the case. However, my advice is to keep at least in mind the assumptions we do when we perform an ensemble or a snapshot measurement and, whenever possible, to check they are valid.
There are grants, there are great words written, there seems to be strong support, but how working between disciplines really work? Let me tell you at least how this has worked for me. This is a long read, but if you do not wish to go at the bottom of it, my advice (sadly) is the advice I once received and did not follow (with no regrets): consolidate your career in one discipline/department/subject (silo?), then you will be free to roam between disciplines at a later stage.
A very early choice to work across discipline*| As a young boy, alongside sports, I picked-up electronics and computers as hobbies leading me to select scientific studies at high school. I then matured a keen interest in physics and biology. When the time came to decide which courses to follow at University, I wanted to combine these interests, applying Physics to understand Life. However, I was undecided if to pursue this growing passion through studies in medicine, engineering, physics or biology. In a very uncharacteristic move for me, as a shy youngster from a family of non-academics and from a town without a university, I found myself sneaking into the Department of Physics at the not-too-far University of Genoa asking to speak with a scientist to get advice. I still remember that a Dr Rossi at the CNR in Genoa explained to me how I could approach my interest following different paths. While I never met again Dr Rossi and I do not recall the details of my visit, on that day and after speaking with him, I decided to study Physics and to become a researcher in biophysics.
Here I got into the first silo | Genoa was an excellent place where to study biophysics as it was one of the towns where biophysics started in Italyand it had a mature and vibrant biophysical community. However, I got an early warning about what meant to work across disciplines. Having opted for Physics, I first had to become a proper physicist, well-grounded in mathematics and theoretical physics. As I generally did well at high school with not too much studying, investing most of my spare time in tinkering with computers, electronics and doing athletics, University was a shock. With no tutoring and no advice (today things have changed), the first two years at University were brutal for me, incapable to cope with the workload and seeing around me, not only friends that were doing well but many who were dropping out (I believe we had a 50% drop-out). Until one day, seating on the floor of the library at Physics… studying maths from a book grabbed from the shelves… breathing pages of old books… when I finally got it. I found my way to study maths, my way to study 24/7. After that mountain was climbed, I picked the few – very formative – courses related to biophysics I could and I finally completed my studies. Although University could have been simpler for me with the tutoring and help that nowadays are available, I am grateful that I was forced to have a very strong theoretical background – no compromise allowed – and I am happy for that first choice of doing Physics at Genoa. However, the first warning was there, unnoticed at the time. To study Life with the tools of Physics, I had studied quantum mechanics, advanced mathematics, particle physics, but I had not a single course in biology or biochemistry. This, despite the fact that what you would nowadays call my master thesis was a year-long experimental work in neurosciences. The fact that I was doing biophysics in a very interdisciplinary environment, partially concealed the fact that science (still) works in silos.
Training at the interface | My choice for a PhD was a bit more random. At the time, I knew I wanted to work with proteins (very vaguely) and I had strong training in fluorescence microscopy. While the search for a laboratory where to do a PhD should be done differently, once again without guidance except for Altavista and Lycos (read as ‘Google’ back then) I identified the first batch of laboratories working with proteins and optics. As my first initial and unplanned search landed me immediate job offers, I was attracted by a very charismatic scientist, Prof. Fred Wouters at the European Neuroscience Institute in Goettingen. My duty was to develop biochemical imaging tools (FRET/FLIM) to study protein-protein interactions relevant to neurodegeneration. At the same time, I enrolled at the University of Utrecht, under the supervision of Prof. Hans Gerritsen, with whom I later obtained my PhD in Physics. Thanks to my struggles at Genoa, I was able to fly, build microscopes, write theory, apply imaging tools to solve biological problems and I completed a successful and productive PhD, by the end of which I was able to do tissue culture and molecular biology as well. Finishing up, on a long train journey to visit my partner who was working in Bonn (also a scientist), I asked myself what I wanted to do and the answer, since then unchanged, became clear: study how cells process information to take decisions by advancing microscopy tools dedicated to the study of biochemical pathways. In that moment I committed to work at the interface and to do both physics and biology.
Swapping disciplines and subjects, the untold dangers| The move for my first real post-doctoral experience was once again insufficiently planned career-wise. At the time, I started to be introduced at talks or in conversations as “one of the top experts in FRET” or “one of the few scientists who can handle biology and physics equally well”. Young experts working across disciplines, particularly with a background in physics and – I suppose today – in Mathematics and Computing, do not have problems to find a job at post-doctoral level. I sent two applications, got two job offers, opted for the one in Cambridge as my wife wished to apply to a lab there. The science (despite not my focus that was still neuroscience) and the environment were very interesting. My work was the attempt to falsify a homeostatic model of red blood cell infected by P. falciparum (the pathogen causing malaria). Once again I was working between disciplines, affiliated to the Dept. of Chemical Engineering and Biotechnology supervised by Prof. Clemens Kaminski and to the Dept. of Physiology, Development and Neurosciences supervised by Dr. Virgilio Lew. Once again, grateful for the training received in Genoa, I flew and I had a very successful and productive post-doctoral experience with my colleagues. However, I started to notice a few more issues.
First, despite the interest and the success, the move to malaria research was not strategic for my final goal and had potentially weakened my profile in the neurosciences. Second, the more senior you become, the more politics counts to seize a position, and without the shelter of a chosen silo (either physics or biology), one might be a bit more at risk. I looked after the former issue seizing an EPSRC Life Science Interface fellowship that I wrote to develop biophotonics tools to investigate the physiological role and interaction of some proteins involved in neurodegeneration.
An unexpected and exciting switch to cancer research | A few months into the fellowship, I was offered to move my fellowship at the MRC Cancer Unit (back then known as the MRC Cancer Cell Unit) where I became, in all effects, a staff scientist. The request was clear, refocus my work to cancer research. EPSRC agreed, and I welcomed the requests as this was strategic to achieve exactly what I planned a few years before, i.e. to study cell decisions by advancing biochemical imaging technologies. My third change of disease model, this time cancer or, more specifically, early oncogenesis, was both very good and bad for me. Very good, scientifically, as it permitted me to align perfectly my scientific ambitions to a disease model where it made perfect sense (cell decisions in cancer are very important and relevant to study). Bad, career-wise, as I once again changed subject therefore further weakening my profile. However, the offer seemed good also in terms of career progression and therefore I accepted. For the third time in a row, my fellowship was a success and productive, achieving my set goals which were, however, more related to advancing technologies while I was getting retrained in cancer biology.
The paradox of the praise of inter-disciplinary research and the silos-like organization of academia | Science works in silos, it still does. These silos communicate, exchange expertise, and they do contribute to beautiful cross-disciplinary work but they are still silos, particularly career-wise. This more or less strongly compartmentalized operation is reflected in the difficulties to review grants, papers, career progression of interdisciplinary work or people at the interface, as discussed in the many articles published on this topic. For now, let me just report a couple of specific events that describes one aspect of the problem.
One day I was at a funding workshop during which several colleagues delivered talks about inter-disciplinary science. One stated that there are excellent people who can do both biology and physics, referring to them as ‘hybrids’. He expressed his support for these hybrids and stated that, as they are rare, we have to fund collaboration between departments. After this comment – delivered by a scientist I have a lot of respect for – I was simply feeling great. Then other speakers clarified how they do not believe in individuals working interdisciplinary but they expressed the need to just collaborate across departments. This – of course – was quite a shock for me. So accustomed to read and hear praises for interdisciplinary work and striving at the interface despite the occasional hic-up and emerging ‘career frictions’, the pieces of the puzzle came together after that event.
The large majority of the Universities, as far as I can tell, are still organized in mono-disciplinary Departments. Even when individual Departments or Institutes are very inter-disciplinary, with biologists, clinicians, chemists, physicists, engineers, computer scientists and mathematicians working shoulder-to-shoulder, you should ask how much disciplinary diversity exists amongst the principal investigators, particularly the tenured academics. If the spread of disciplines suddenly shrinks to a few very related backgrounds, you would have a clearer picture of how interdisciplinary work is rewarded.
This is summarized by a comment I once heard at a conference. After a number of talks about magnetic resonance imaging at the University hospital, and the praise of mathematicians (PhDs and post-docs) who contributed so much to the progress, one person from the audience popped the magic question: “which career perspective do you offer to these young mathematicians without whom this progress could not have been achieved?”. The response was delivered bluntly, honestly and respectfully: “None. We do not have possibilities for career advancement for mathematicians but most of our PhDs and post-docs after working with us do well in industry”.
I am absolutely sure there are plenty of exceptions to what I am describing. However, I do not think I would be too wrong to warn you, perhaps a younger-me, of the risks in leaving the shelter provided by a well-established silo, at least from a career perspective. A silo where career structures might be clearer and career progression might be still very difficult but more ‘natural’.
Am I in the wrong silo? | The last chapter of my story (for the time being) is still writing itself. More importantly for those two of you young readers landing on this page, I should clarify that it is a story were many plots get entangled. I wished to answer questions such as “how was your experience working at the interface of life and physical sciences?” or “how was for you swapping between different disciplines”. However, the longer you stay in academia, other issues arise such as reaching job security, finding a good balance between family and work, maintaining/finding/expanding resources (people, funds, space, instrumentation,…), supervising/mentoring people, finding a balance between research and other academic duties, etcetera. These and other important aspects of our work are common to any scientist, irrespective of how many disciplines or subjects they touch. However, working at the interface between disciplines adds – in my opinion – a little bit of friction to most of these processes.
I am doing biomedical research in a cancer research institute, I love it and I enjoy working with my colleagues. However, I am a biophysicist with a strong track record in biophotonics, not much track record in cancer biology. After the successful completion of my EPSRC fellowship, I was expecting to get into a tenure track position with dedicated resources. However, the new (however obvious it might appear writing it down now) condition I had to confront was to have a track record in cancer research possibly with high impact factor journals. Retreat to the ‘shelter’ of Physics departments or competing on this new ground of biomedical research on the game (that I do not even like nor endorse***) of impact factors? While the choice should be obvious, I personally focused only on the scientific ambitions, trying to establish what I like to call a “single-cell systems biology of cellular decisions” and I opted, somehow reluctantly, to play the game. I am sure that others would have handled the situation better. Personally, I enslaved all my physics/engineering/mathematics to the solution of biological questions and stopped publishing specialist work. At the same time, caving-in to peer pressure, I focused on preparing manuscripts that, potentially, might be published in high impact factor journals entering a very long cycle of ‘stashing’ data seeking to have the most solid work and the most interesting narrative (I shiver spelling it out, and I corrected this by using pre-print servers and resuming publishing specialist work).
Not only the work I excel into is invisible to most biomedical colleagues, erroneously tagging it ‘just technology or methodological’. I mistakenly reinforced this trend by starting to bury a large part of my work in the supporting information of would-be high impact-factor journal papers. Somehow, the need to fit in my environment, the expectation of peers in cancer biology, referees and panels, made me behave as if I should be ashamed of the work I am actually best known for. The issue is not my institution, certainly not the very supportive colleagues. Perhaps I am simply in the wrong silo in an academic environment that works as communicating silos. By now, however, I would be in the wrong silo in most academic silos and I shall continue attempting to prove there is a reason to have some ‘hybrids’ working at the interface between disciplines.
A war of attritions| I shall conclude with a comment on something I believe is important for anyone that is ‘different’ in an academic environment, something I will expand upon in the future in a different context. In any very competitive environment, and Academia as I know it is highly competitive, the best might emerge. However, people of the same quality will experience different frictions. For example, even in the absence of outright discrimination, gender, ethnicity, nationality, religion, physical ability or even regional accent might each result in a additional friction while climbing up in career depending on the environment**. Working at the interface between disciplines, or swapping discipline, will help to make you unique but, at the same time, it might add significant friction to your walk through Academia. While I have no regrets and I love – as a physicist – working on cancer biology in a biomedical research campus, I wished to warn those scientists willing to do the same of the possible hidden risks. Of course, this is just my story, but there is plenty of research out there showing how difficult is to work across disciplines for both individuals and teams.
You will love breaking free from the cages of disciplines. You will feel strained by the absence of a safe shelter.
So, perhaps, the solution is one I was advised a decade ago, the advice I neglected as I assumed was given for self-interest. You might want to first establish yourself within a single discipline, be either physics or biology for example. Once you will have a well-established career, you will be able to use resources across disciplines.
That was not for me, I am a ‘hybrid’ after all.
* Be aware that in this blog-post I use various terms to refer to working across disciplines (inter, cross, multi, etcetera) I do this in a very colloquial way.
** I do not intend to compare the issue of inter-disciplinary research to the struggle of asserting civil rights! My point here is simply that in the absence of outright discrimination (for those environments where this might be applicable) unconscious bias might remain thus adding some friction to the career of people. Bias against multi-disciplinary research is well-characterized in the literature and, I argue, this bias is yet another friction that adds on to the normal challenges in academic progression.
*** I should clarify that I do not have anything against high impact factor journals. They are a business and they do it well. Moreover, they often provide a great editorial input and production assistance. However, I am critical on the use of such journals in Academia that, in practise and in many cases, slows down the discovery process.
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 students 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 precariat state. 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 has 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 promotes 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. ■