Once in a while I hear or read about dogmas as if they were models. I came to realize that some people might not be aware what a dogma is and before the (mis)use of this word spread even further, I hope you will agree to get it back into its original meaning.
The Oxford dictionaries define dogma as “A belief or set of beliefs held by a group or organization that others are expected to accept without argument”. Other dictionaries report similar definitions, but the Merriam-Webster also include the slightly softer “Something held as an established opinion, especially a definite authoritative tenet”. Many dictionaries also report to religious doctrines. Therefore, dogma can’t be used as synonym of model or hypothesis, particularly in science. Of course, most people are still using the word dogma correctly even in science, to refer to a model that has become established fact despite no, weak or even erroneously interpreted evidence for it.
I suspected that most of the damage has been caused by Francis Crick when he has introduced the “Central Dogma ” of molecular biology. Let’s be clear, I do not want to be pedantic and I care very little about semantics, but the correct use of the words dogma, hypothesis, model, theory, is rather important in science. There are instances when these four words might be interchanged but we should – I hope – all agree that dogma is to be used only with a negative connotation (in science).
I assume you know what the central dogma is but if you do not, the Wikipedia page is good enough to get an understanding. In lectures during the late 50s, Francis Crick stated that “Once information has got into a protein it can’t get out again” and named this statement “The Central Dogma”. Apparently the name was a bit of a joke, as it appears evident from the famous document stored by the Wellcome Library. The initial paragraph was entitled “The Doctrine of the Triad”, a clear reference to DNA, RNA and proteins with a rather obvious analogy to the Christian doctrine of the Trinity.
I must admit I did not read Crick’s autobiography, but it is well known that there, he writes that “I called this idea the central dogma, for two reasons, I suspect. I had already used the obvious word hypothesis in the sequence hypothesis, and in addition I wanted to suggest that this new assumption was more central and more powerful.” and “As it turned out, the use of the word dogma caused almost more trouble than it was worth. Many years later Jacques Monod pointed out to me that I did not appear to understand the correct use of the word dogma, which is a belief that cannot be doubted. I did apprehend this in a vague sort of way but since I thought that all religious beliefs were without foundation, I used the word the way I myself thought about it, not as most of the world does, and simply applied it to a grand hypothesis that, however plausible, had little direct experimental support.”
Then, I asked a friend who lived those times if perhaps the word dogma was used slightly differently in the past and I got this brilliant response: “A dogma in science is a fanatic intrusion into rational thought. When a big name in science makes a joke, accolades of small names taking it seriously are sure to follow… A model becoming a dogma is ready for the bin. Never had a dogma crossing my path.”
Well, nowadays I do see dogmas crossing my path but never mind, that is a different story. For the young students who might read the “central dogma” in text books and then adopt the term “dogma” as equivalent to model or hypothesis then, just two suggestions.
First, a scientists should be always skeptical and doubt about anything. It is unavoidable that sets of established facts, sometimes even wrong, become generally accepted in a discipline and crystallize into a real dogma that no one challenge. However, it is our duty to challenge any interpretation, any model, whenever is conflicting with evidence.
Second, let’s reserve the word dogma (in science) to critically identify established believes with insufficient or contradictory experimental evidence, or perhaps for jokes…
Ironically, the “Central Dogma” was a very good hypothesis.
Why should you know FRET? Well, FRET is used when you do a real-time qPCR, or you might be using it in assays like HTRF, or to detect biochemical reactions in single living cells. You might measure protein-protein interactions, probe cell signalling, cell metabolism or nano-meter scale conformational changes. Or what about dimerization, protein – nucleic acids interactions, checking splicing variants by FISH, or detect fast conformational changes in structural studies? This is why some of us are very fond of FRET, and many others are using it without being fully aware of it. The usefulness of FRET arises from its capability to translate molecular properties occurring at a nanometer and nanosecond scales to optical signals that can be easily detected with a microscope or a spectrofluorimeter.
What is FRET? When a fluorescent molecule is in close proximity to another that might be, in principle, capable to absorb the light emitted by the first, FRET might occur. However, FRET is not the emission and re-absorption of light, but the non-radiative transfer of energy. This is important because the molecule that will donate energy and the one that will accept it become coupled and will inform us about the distance between the two molecules only if they are within a few nanometer ranges, with sub-nanometer precision. Most of us do not use this capability directly but to engineer probes that can sense specific biochemical reactions. Ok, now you are ready. What FRET stands for? RET is Resonance Energy Transfer and it says with three simple words what I have just described. For the “F”… you would think it is simple, but the community is a bit split on the meaning of that “F”. There are two camps. One that says “F” is for Foerster, from Theodor Foerster who developed the theoretical background to describe the phenomenon. Others say that “F” is for “Fluorescence” as it is detected by means of fluorescence emission. Who prefers Foerster-type energy transfer means to distinguish it from other possible mechanisms but, most importantly, to avoid misinterpretation of the acronym. Indeed, it is not fluorescence that is transferred from donor to acceptor and the acceptor does not need to be fluorescent. Those who use Fluorescence RET often say that Foerster did not discover FRET (correct, he did a mathematical description of a known phenomenon). Does it matter? Not really, but at least now we know what FRET means. Ah, I almost forgot… FRET for me is Foerster Resonance Energy Transfer… I heard you asking.
Next. How do we measure FRET? There are many ways to measure the occurrence of FRET but today I will focus only on ratiometric FRET and Fluorescence Lifetime Imaging Microscopy (FLIM). I am going to use an analogy that is very useful, that of buckets filled with water (Fig. 1). The tap is your light source, which is filling a donor bucket with water (energy). The bucket has one hole, from which water is dripping into a plate (a detector). That stream of water highlighted in green in Figs. 1-2 is the fluorescence signal that we measure, emitted by the donor. FRET is another hole punched into the donor-bucket. Water will flow into an acceptor-bucket from where it will drip (red flow) into a second plate (detector). The ratio of the water we collect in the blue and yellow plates will tell us the fraction of water that passed through the FRET “hole”. In a real FRET experiment, this fraction, called the ‘FRET efficiency’ is proportional to the inverse of the sixth power of the distance between the buckets, er… fluorophores.
Unfortunately, the excitation and emission spectra of typical fluorophores are broad and spill-over of fluorescent signals (or water!) is usually unavoidable (Fig. 2). The buckets are large compared to their distance (the excitation spectra overlap) and part of the water we wish to put into the donor bucket will fill the acceptor bucket. This is called ‘direct excitation’ of the acceptor. The water we now collect in the yellow plate flows from one hole in the acceptor-bucket, but it originates from two different flows. Direct excitation (black flow) and FRET (red flow). The latter, FRET sensitised emission, is the signal that matters. At the same time, water flowing from the donor bucket spills-over into the yellow plate (the emission spectra overlap), adding a third (green) unwanted flow into the yellow plate.
So, how do we correct cross-talks? The good news is that sometimes you do not need to. If what you need to measure is a semiquantitative measure, the detection of changes, measuring the relative quantity of water that fell into the yellow plate compared to the blue plate will suffice. This, however, will require to ensure the stoichiometry of donor-acceptor fluorophores does not change, for instance when using typical FRET-based probes for kinase activity.
In other cases, you will need to correct for these cross-talks and techniques like ‘precision FRET’ and ‘three cube FRET’ comes to the rescue (see reference section).
Another technique that can be used to measure FRET is Fluorescence Lifetime Imaging Microscopy or FLIM. FLIM does not need measuring the flow of water from the acceptor. FLIM requires to turn the tap on and off, and measuring the time that the donor buckets requires to be emptied. When a second hole (FRET) is punched into the donor-bucket, this will empty faster. We do not measure directly any signal from the acceptor and, therefore we avoid the need to correct for spill-overs.
This brings me back to the time I was a PhD student. A very smart master student entered my office and popped the question “how FLIM can detect the presence of FRET if the only photons we measure are those that do not experience energy transfer?”. Back then, I was taken aback from the question and I could not respond immediately in a satisfactory way. The bucket analogy should do the trick.
To conclude, this was just a brief overview of FRET and how we can measure it. There are plenty of great reviews out there to improve your understanding of FRET, but I hope that the analogy with buckets might provide a simple model for the non-specialist, albeit physically inaccurate for other aspects of FRET. Below, you can find a few references. Let me also refer to my new study published on Biomedical Optics Express entitled “How many photons are needed for FRET imaging?”. It is a theoretical study, but even the non-specialist might find some sections interesting and, plenty of more bucket figures there!
SOME USEFUL REFERENCES
J. R. Lakowicz, Principles of Fluorescence Spectroscopy (Kluwer Academic/Plenum Publishers, New York, 1999).
T. Förster, “Zwischenmolekulare Energiewanderung und Fluoreszenz,” Annalen der Physik 437, 55-75 (1948).
L. Stryer and R. P. Haugland, “Energy Transfer – A Spectroscopic Ruler,” Proceedings of the National Academy of Sciences of the United States of America 58, 719-& (1967).
G. Bunt and F. S. Wouters, “Visualization of molecular activities inside living cells with fluorescent labels,” International Review of Cytology 237, 205-277 (2004).
E. A. Jares-Erijman and T. M. Jovin, “FRET imaging,” Nat. Biotechnol. 21, 1387-1395 (2003).
J. Zhang and M. D. Allen, “FRET-based biosensors for protein kinases: illuminating the kinome,” Mol Biosyst 3, 759-765 (2007).
M. Y. Berezin and S. Achilefu, “Fluorescence lifetime measurements and biological imaging,” Chem Rev 110, 2641-2684 (2010).
A. D. Elder, A. Domin, G. S. Kaminski Schierle, C. Lindon, J. Pines, A. Esposito, and C. F. Kaminski, “A quantitative protocol for dynamic measurements of protein interactions by Förster resonance energy transfer-sensitized fluorescence emission,” Journal of the Royal Society, Interface/the Royal Society (2008).
A. Hoppe, K. Christensen, and J. A. Swanson, “Fluorescence resonance energy transfer-based stoichiometry in living cells,” Biophys J 83, 3652-3664 (2002).
M. Elangovan, H. Wallrabe, Y. Chen, R. N. Day, M. Barroso, and A. Periasamy, “Characterization of one- and two-photon excitation fluorescence resonance energy transfer microscopy,” Methods 29(2003).
G. W. Gordon, G. Berry, X. H. Liang, B. Levine, and B. Herman, “Quantitative fluorescence resonance energy transfer measurements using fluorescence microscopy,” Biophysical Journal 74, 2702-2713 (1998).
C. Berney and G. Danuser, “FRET or no FRET: A quantitative comparison,” Biophysical Journal 84, 3992-4010 (2003).
J. Wlodarczyk, A. Woehler, F. Kobe, E. Ponimaskin, A. Zeug, and E. Neher, “Analysis of FRET signals in the presence of free donors and acceptors,” Biophysical Journal 94, 986-1000 (2008).
A. Zeug, A. Woehler, E. Neher, and E. G. Ponimaskin, “Quantitative intensity-based FRET approaches–a comparative snapshot,” Biophys J 103, 1821-1827 (2012).
H. C. Gerritsen, A. V. Agronskaia, A. N. Bader, and A. Esposito, “Time Domain FLIM: theory, Instrumentation and data analysis,” in FRET & FLIM Imaging Techniques, T. W. Gadella, ed. (Elsevier, Amsterdam, The Netherlands, 2009).
R. A. Neher and E. Neher, “Applying spectral fingerprinting to the analysis of FRET images,” Microscopy Research and Technique 64, 185-195 (2004).
H. Wallrabe, Y. Chen, A. Periasamy, and M. Barroso, “Issues in confocal microscopy for quantitative FRET analysis,” Microscopy Research and Technique 69, 196-206 (2006).
S. Ganesan, S. M. Ameer beg, T. Ng, B. Vojnovic, and F. S. Wouters, “A YFP-based Resonance Energy Accepting Chromoprotein (REACh) for efficient FRET with GFP,” Proceedings of the National Academy of Sciences of the United States of America 103, 4089-4094 (2006).
J. Klarenbeek, J. Goedhart, A. van Batenburg, D. Groenewald, and K. Jalink, “Fourth-generation epac-based FRET sensors for cAMP feature exceptional brightness, photostability and dynamic range: characterization of dedicated sensors for FLIM, for ratiometry and with high affinity,” PLoS ONE 10, e0122513 (2015).
K. J. Martin, E. J. McGhee, J. P. Schwarz, M. Drysdale, S. M. Brachmann, V. Stucke, O. J. Sansom, and K. I. Anderson, “Accepting from the best donor; analysis of long-lifetime donor fluorescent protein pairings to optimise dynamic FLIM-based FRET experiments,” PLoS ONE 13, e0183585 (2018).
M. W. Fries, K. T. Haas, S. Ber, J. Saganty, E. K. Richardson, A. R. Venkitaraman, and A. Esposito, “Multiplexed biochemical imaging reveals caspase activation patterns underlying single cell fate,” bioRxiv, 427237 (2018).
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.