Covid (v3) – data visualizations

WARNING. I am not a medical doctor nor an epidemiologist. The analysis I am sharing here is only for the data geeks around that are curious. Please follow the advice of your national authorities and health system.

Let me publish this short update on the COVID pandemic to share some of the most interesting visualizations I found online and the last update of Matlab code for those of you who would like to play with the data. For some of us, keeping an eye on the data is a way to diffuse anxiety about the pandemic and it is in this spirit I keep posting, now and then, comments on COVID. If data, trajectories, comments on the pandemics are stressing you and you are experiencing a COVID overload, perhaps you would like to read Prof. Aisha Ahmad’s article – particularly if you work in higher education. And… not read this far less useful post of mine.

First, NextStrain is a tool that permits to monitor the spread of various infectious diseases, including CoV-2019 (thanks to Quentin to share the link). You can appreciate how the virus is mutating and how different clusters/mutations are geographically spread. I should note that, to my knowledge, there is no evidence for differences in the severity of the disease depending on mutations identified so far. These differences are currently used just as simple fingerprints, or ‘paternity tests’ to identify the evolution and the spread of the virus. I avoid interpretations of this data but you can find reports on NextStrain. I assume that these maps are biased significantly according to the amount of sequencing that each country is doing. You might appreciate the central role of, of course, China, but also of the UK as a hub of transmission. This might be because of London, an important aviation hub, and the high numbers of sequencing happening in the UK, but neglecting the number of samples, if the timing/trajectory of the spread will be confirmed in due time, it will be very interesting to understand the implications.

Before showing a bit of new Matlab code I worked on in the little spare time I had lately, I would like also to introduce the work that inspired my new analysis. There is this very nice tool by Aatish Bhatia and a great introductory YouTube clip by the same author.

Like most non-expert people, I am interested in understanding the efficacy of public health measures taken in different countries. As confirmed cases are rather unreliable, and the likely cause of the big differences in mortality rates across countries (see previous posts), I currently focus only on reported fatalities. It should be noted that this data is more reliable, but again it depends on the reporting criteria of each individual country and these might change. Hence, the true impact on public health will be clear only retrospectively in a year or so, but for the time being, we can appreciate trends to get an impression if containment measures are working or not.

First, I would like to start with the raw data. Here I present groups of selected countries based on my own interest but with the Matlab code available on GitHub you can generate the same graphs for the groups of countries of your choice using the Johns Hopkins CSSE dataset. To analyse just the raw data Bahtia’s tool is easier to use, but I show here the results to introduce my next analyses. Please note that the data is averaged over a sliding window of three days and we usually get data with 1 day of delay. Therefore, the curves are representative of the situation as it was 2-4 days ago.

These graphs are difficult to interpret as countries not only are experiencing different types of epidemics (for example, more or less localised) but do have also rather different population sizes. However, these trends allow us to understand – broadly speaking – if the policies enacted by individual governments are providing the expected results.

You might notice that I present two entries for China: one for China overall, and another just for the Hubei province. Here, the two graphs are almost identical because the Chinese outbreak started in Wuhan, within the Hubei province, and there caused many more fatalities. The black line is just shown as a reference to guide the eye. When the traces significantly deviate from this line, eventually tracing a horizontal line, confirmed deaths stop increasing exponentially. In that phase, the epidemic is still causing fatalities but it becomes more manageable and predictable. Most importantly, for those countries in lockdown, it is the clear sign that the strategy is effective. For those countries that are not in lockdown, for example, the Republic of Korea (aka South Korea), such trend imply that the epidemics is not resolved but controlled to strike a balance between socioeconomic sustainability and control of the epidemics.

The second gallery is a collection of the very same plots but normalized to the total population of each country. You might appreciate here why I report data for both Hubei alone and mainland China overall. I heard – amazingly even from virologists – that the data from China is not correct because China cannot possibly have so few fatalities being such a large country. At this stage, we can’t trust data as fully reliable from any country, but it should be clear that the epidemics was initially localized in Wuhan, Hubei. There it went out of control but in the rest of China a combination of the lockdown and tracking of patients made possible to avoid an uncontrolled epidemics. This is why Hubei, with its 60M inhabitants, should be considered as a reference and not all mainland China and why I am reporting the two curves.

Normalizing fatality by population size, we can now appreciate how some countries are in much similar state at the moment in Europe with, of course, plenty of exceptions. Another note on the reliability of data. All of us, even the non-specialists like me, have learnt how ‘confirmed cases’ of covid are a rather unreliable indicator because of different capacities in each country to test, particularly in different stages of the epidemics. Reported fatalities are a more robust indicator. However, different countries might adopt different methodologies to report ‘confirmed’ cases. Some countries make a distinction between people who died with COVID and people who died of COVID, but others do not. Some countries are faster than others in reporting and deaths outside hospitals – anywhere – are likely to be counted with a significant delay, or sometimes not reported at all because of lack of testing. Therefore, keep in mind that data from any country is not rock solid, and we will discover the real impact of COVID only at a later stage.

Most importantly, this is not a race between countries. Each of these numbers is a human life cut short and a complex network of relations broken. Therefore, my comments are provided as a means to try to understand what is happening from the standpoint of the layman I am in this context, with the deepest respect of what ‘fatalities’ means.

This noted, I wished to present the last gallery. Different countries have different demographics and the risks, as we know, of COVID are age-dependent. Therefore, I used the age-dependent mortalities inferred from mainland China (Hubei excluded – this represents a best-case scenario) to provide a rough estimate of the population at risk in each country. For example, once adjusted for the different demographics, an average fatality rate in China would amount to about ~1% but ~2% in Italy and 1.7% in the UK. Notably, the mortality rate in Hubei was higher (~4%) and using this value for Italy we would expect ~9% mortality. However, many have noted how these values are unrealistic and heavily depends on testing capacity, that is constrained during phases when a health system is overwhelmed. Most studies estimate fatality rates around 0.5% to 1%. If 1% is the real mortality rate, to evaluate the population at risk in Hubei, we can simply take 1% of the ~60M inhabitant as the population at risk. For all other countries, this proportion is made considering the different number of inhabitants in different age bands.

The take-home message is that the actions that governments decided to take are having the desired effects. While I might disagree with one policy or another, I thus invite people to follow the guidance provided from each country. However, it will be interesting to keep comparing the Netherlands and Sweden with other European countries, and China with other countries in South East Asia as, by choice of necessity, different strategies have been employed. Data from the UK and Sweden (and others) is rather noisy, with some temporal variation that might depend on the way data is reported. Therefore, it is to early to tell how the situation is developing in these two countries, but over the next week, a picture will be rather clear. We have now to watch out for the US and also the many countries that I did not study so far but that will play an equally important role in the evolution of this disease.

Personally, I had supported a fast initial response. While trying to shrink the epidemics, I hope that countries now will cooperate to share resources to save as many lives as possible. At the same time, I hope that countries will also cooperate in rebooting the world economy and productivity as soon as possible. We should not rush to not waste all the work done but we should have clear plans to remerge from lockdowns.

We are still adapting to this new reality. However, while supporting our societies in passing through this public health issue, as soon as we’ll see those trajectories dropping (or before if you can), we will have to quench the pandemics of hate that might break out between countries. We can do that only by resisting the populist trends we had been already experiencing, dark energies that might be getting stronger than ever.

I hope, instead, that we will feel closer to each other. In our streets, in our nations, with our neihboring countries but also with those far far away who suffered or will suffer like any of us. Together, we can build a brighter future. Against each other, more and more lifes will be lost.

Corona virus – data mining (v2)

WARNING. I am not a medical doctor nor an epidemiologist. The analysis I am sharing here is only for the data geeks around that are curious. Please follow advice of your national authorities and health system.

NOTE: For a more comprehensive blog post, you might be interested in Tomas Pueyo’s website. A good discussion about mortality rates can be found at CEBM website.

The trends

I have run a comparison between selected countries. I use Hubei (the Chinese province where Wuhan is located, in red) and Italy (in green) as references. The top plot in each figure shows confirmed cases and the middle plot shows covid-related deaths. Check the appendix for a discussion on the bottom plots (mortality rates). The JH data starts with Hubei on the day that the province went into lockdown. I will often comment on the comparison between the UK and Italy as I am a British-Italian dual-national and I started to follow the data to understand how to adapt to the situation.

Day 1 in these plots is relative to the start of Hubei tracking, which coincides with the day of Wuhan lockdown (red vertical line). The vertical green lines represent two key moments of the Italian response. The first is the local lockdown of towns where outbreaks in Northern Italy occurred, the second is the national lockdown. The vertical blue lines are key moments of the British response. The first one marks the day when the PM announced abandoning the containment strategy to pursue ‘herd immunity’, the second line marks the day when a UK nation-wide lockdown was implemented.

The comparison between Hubei, Italy and the UK is interesting as these are regions with similar total populations (~60M). Imported cases are identified at a similar time in both UK and Italy, but the two countries then follow quite a different trajectory. Until day 35, the UK seems capable to track and isolate cases, after which local outbreaks are evident from the start of a steep exponential (linear in this log-scale) growth. Instead, Italy experienced a sudden (apparent) outbreak at day 30. The difference between Italy and UK at this stage is that Italy shut flights from China (contrary to WHO indications) and kept testing only people with a declared travel history from China. This caused the coronavirus to spread undetected, particularly in the hospitals that did not trigger emergency procedures timely. It seems likely that the coronavirus was imported from Germany by people returning from China through Germany. However, it seems also likely that the spread was boosted by contacts between Germans and Italians, between strong productive regions, completely bypassing the Italian monitoring strategies, too focused on China. From the trends of detected cases in Italy, one could extrapolate that the local epidemic starts immediately and undetected as soon as the first imported cases are identified. The good British monitoring of the epidemic gives the UK two weeks of breathing time for preparing to the epidemics.

In my previous post, I mentioned about the puzzle of Germany exhibiting low mortality and some reports about Germany counting covid-related deaths differently from other countries. Differences in counting are probably real but I am now convinced that too much emphasis on this point is unadvisable. My impression is that there is a lot of talking about this to spread fake news aimed to construe a conspiracy theory about some countries hiding covid-cases to protect their economies. Personally, I wished someone would make clarity on how deaths are counted, but I am keen to interpret – at this point in time – data as more realistic that some people might consider. Probably, Germany did simply a better job than the UK and Italy and delayed the local epidemic of three weeks compared to the latter.

We can browse the plots of different western European countries and notice similar trends, with different delays caused by the probability to import cases and the capability of each country to track and contain imported cases. Amongst the countries I check, Spain is the outlier, with a steep rise of covid-related cases that has been broadly described in the media and not yet fully explained.

In South East Asia (I checked China, Singapore, South Korea and Japan), the trends are very different from Europe. We can argue that different political and societal systems permitted a better response. In general, we can state that they responded fast and proactively. You might notice second waves of infections. Japan, Singapore and South Korea did not enforce national lockdowns but are containing the epidemics with careful tracking and strong mitigation measures. Somehow it is what the UK wanted to do. However, the UK, in my opinion, acted too late and when they announced changing from containment to mitigation plans, it was too late to adopt a balanced strategy between public health and economy (the South East Asian model) and too late to fully contain the disease with low casualties. Therefore, western European countries will likely follow the Chinese trend, full containment followed – in due time – with the South East Asian response unless a cure or vaccine will be ready early.

Singapore is the outlier, however. We should consider that Singapore has only 10% of the inhabitant in Italy and Hubei (Japan twice, South Korea similar), it is, in fact, a high-density population city-state. The death rate is very low… I wonder how the situation will evolve and how the data is collected. Back to Europe, we should keep an eye on Sweden as – if I understood correctly – the Swidish government did what the UK wanted to do, i.e. mild mitigation measures.

Let’s conclude this overview with USA. We should keep in mind that in any country, the statistics are the sum of multiple local outbreaks. USA has >300M inhabitant split in 52 states. For the time being, I just check the overall trends. And the trends are simply alarming. Of course, I neglected many countries but you can freely use/adapt my Matlab code on GitHub to draw your own conclusions.

Next…. let’s synchronize the curves.

Contrary to my previous blog post, I am now synchronizing the curves on the death statistics, as they are more realistic, while confirmed covid-cases heavily depend on the capability of health systems to run tests across the non-hospitalized population. I used an arbitrary number of deaths, 40, sufficiently large to provide a robust synchronization, but sufficiently low to precede containment actions of governments that would, of course, change the shape of the curves. Most South-East Asia countries are not shown as they are containing the epidemics at the moment. With this data, now you can appreciate that the epidemic across European countries is very similar with the caveat that any type of synchronization of data is wrong, certainly if not done with proper models and study of local outbreaks. However, the general conclusions might not change.

Rolling back to the UK, Italy and Hubei, you can see that the British government announced the abandoning of the containment phase, exactly when Hubei did the opposite (in relative time). After initial errors, Italy acted fast but had to play a chase game with the outbreak. Their strategy was not working and Italy had to enforce a nation-wide lockdown that started about 10 days later in comparison to Hubei. A nation-wide lockdown might not have been necessary, but the lack of compliance of the general population, some doctors insisting that coronavirus was not worse than the flu and some political parties trying to score points rather than supporting a common strategy eventually required a national lockdown that now is finally having the desired effects. We can be still concerned, however, about possible second-waves of the epidemics also in the short terms because of waves of inner migration from North to South, people running away from the epicentre of the Italian outbreak, although the current lockdown might be able to quench new outbreaks. We can just wait and see for now. However, that delay and initial lack of cohesion will result in several thousand preventable deaths.

In the UK, the first stage of the epidemics is still unfolding. It is interesting to observe that about a week after the British government decided not to take action to reach ‘herd immunity’, the increase in deaths deviated from the original trend, slowing down. On the ground, we have noticed how a large majority of people, seeing what was happening in Italy, took action independently from government advise. Sport organizations cancelled events, Universities started to close, people increasingly worked from home and several families started to withdraw children from schools. In all effect, the British population started social distancing measures ahead of the Government that, eventually triggered a national lockdown approximately at the same (relative) time than Italy.

One issue that in Italy has been publicized but not in the UK is what I referred to as internal migration. The reason is simple. Paradoxically, in both countries this happened because people did not follow advice. In Italy, the Government asked not to travel from the afflicted regions. People did not follow advise and when the government was preparing the regional lockdown, an opposition party leaked the measure to the press. People panicked and travelled to their holiday homes and University students went back to their family, many travelling from North to South. In the UK, the Government did not ask to close University, but Universities did close and students were invited to go back to their families. This advice was sensible as we do not want to have students trapped in student accommodations. However, this happened without control and without a good sense of the status of the local epidemics. Therefore, while not broadly reported by the media because of how this happened, this large movement of people might contribute to the future dynamics of the UK epidemics, and possibly also in other countries.

It is just my opinion, not a scientific fact for now, that the UK wasted an incredible amount of time. While the NHS and the Government might have done everything right initially, all those efforts had been squandered. What I argue here is that no strategy is a good strategy in these circumstances. Coronavirus will kill a lot of people and cause damage to the economy. Adapting a strategy is also important, new information might require a new strategy. However, contrary to the story depicted by the Government, there was no change of science about the epidemics. A U-turn in policy can be costly as it might result in none of the positive outcomes desired with either one or the other strategy.

The only hope is that, those countries that were able to delay the onset of local epidemics compared to Italy and Iran, either by chance (low exposure) or by good management, were able to scale-up contingency plans and availability of specialist ICU beds to draw the final numbers of deaths as low as possible. Why this was not arranged at the onset of the epidemics is something we will have to analyse in the future.

Soon, we will speak a lot about USA. China has four times the population of USA. China was able to contain the disease (for now) and to avoid widespread diffusion across all China. Outbreaks after outbreaks, the trends from USA are accelerating and already overshooting both Hubei and China overall, with no sign of slowing down of the epidemics.

Concluding remarks

In the next sections, I describe the methods I used and I provide a discussion about mortality rates. However, I wished to conclude my post here stating that this might be the last time I post graphs, as by now there is so much data around and from people with a background in epidemiology. However critical I might be of certain political decisions, I would like to be very clear on the following. The individual risk to people is comparatively low. The large majority of the population has to fear more the socioeconomic repercussions of the pandemic rather than the disease in itself. The socioeconomic impacts of nationwide lock-downs will impact people’s health. Mental health will deteriorate. Health systems will be weakened and therefore more people will suffer even if not infected. Governments all over the world are trying to guess what is best to do. My advice is to follow guidance from your national medical and governmental authorities. It might seem I am contradicting myself. I am not. I simply acknowledge there is no good solution to the problem. However, governments should speak the truth and clarify why they take certain decisions, at least in democracies. Governments should also work together not against each other. Some of us were fearing a big war was brewing after the 2008 financial crisis but we were not expecting a pandemic. Now that we got the pandemic, I hope we do not get both, but that this situation is bringing all peoples of all nations together. At the moment there are both good and bad signs. In Italy, we have a saying: “La speranza e’ l’ultima a morire”. Hope is last to die. And we hope, we hope for more rainbows at the windows and fewer clouds at the horizon.

Take care, my friends.

The data | The data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering is available on GitHub.  To adjust mortality rates by local demographics, I have downloaded the population pyramid data from There are several estimates for age-dependent mortality. I was able to find only the following pre-print for mortality in Hubei compared to the rest of China. The dataset analysed was small, let me know if you find something better.

The software | In spare time, I prepared a bit of Matlab code that can import the JH data and does just two simple things: compare trends between different countries and compare age-adjusted moralities. The code is available on GitHub. Keep in mind, sorry to repeat, this is just for the curious geeks.

Appendix – Mortality rates.

Let’s now discuss, briefly, mortality rates. This is a very complicated issue, not just for the non-experts like me. We will have better estimates only much later. At the moment, there is no evidence about the existence of multiple strains of CoV-SARS-2 exhibiting different aggressiveness. There are of course different strains, as viruses do constantly mutate, but the idea that some country is more affected than others because of different strains – at the moment- seems to be just a way to justify their own shortcomings. Of course, we will understand this later.

Let’s consider Washington state (not shown here) that resulted in a very high mortality. This was the result of very little testing of the general population, and a spike of deaths of elderly people arising from outbreaks in retirement homes. New York is on the opposite scale as it was a more ‘standard’ outbreak. We noticed mortality rates going from very high to very low and bouncing back. These are all artefacts of sampling.

I re-propose here some graphs comparing demographics. First, the age-adjusted mortality. The blue bar in China at ~1% is the mortality rate in China outside Hubei. This, in my opinion, is a good measure and backed up by epidemiological studies. WHO is setting this number at ~3.5% because at the moment they are just dividing confirmed deaths by confirmed cases, a number likely to be overestimated.

The blue bars for other countries are adjusted to the different demographics of individual regions. Italy has an older population compared to most other countries and, therefore, higher mortality is to be expected. There is some report suggesting that also men are more likely to die compared to women. Therefore, in the middle graph, I compare the demographics of Italy, UK and Germany to China. The red bars in the first graph are the mortality rates inferred from Hubei, thus in a situation where the health system is overwhelmed. As you can see, in Italy we can expect apparent mortality rates of almost 10%. Indeed, at the moment this value has been exceeded. However, here the keyword is ‘apparent’.

Therefore, while I might be still discussing COVID in this blog, I will probably stop speaking about mortality rates. Eventually, these numbers could be very low. Perhaps, we might have ‘just’ 0.5% of the population at risk of death, maybe 1% in countries with older demographics. However, keep in mind that like the WHO has always said, the issue is the overwhelming of the health systems. UK, Hubei and Italy have around 60M inhabitants. This means that a ‘do-nothing’ policy would result in 300-600k deaths in each country (40M world-wide). Or half of that if some sort of herd immunity would protect us. To put this in perspective, ~500-600k people die in the UK every year (60M world-wide).

Coronavirus – data mining

WARNING. I am not a medical doctor nor an epidemiologist. The analysis I am sharing here is only for the data geeks around that are curious. Please follow advice of your national authorities and health system.
NOTE. This post was updated on 15/3.

The data | The data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering is available on GitHub.  To adjust mortality rates by local demographics, I have downloaded the population pyramid data from There are several estimates for age-dependent mortality. I was able to find only the following pre-print for mortality in Hubei compared to the rest of China. The dataset analysed was small, let me know if you find something better.

The software | In spare time, I prepared a bit of Matlab code that can import the JH data and does just two simple things: compare trends between different countries and compare age-adjusted moralities. The code is available on GitHub. Keep in mind, sorry to repeat, this is just for the curious geeks.


I summarize a few countries I checked. As I am Anglo-Italian, and with Italy and UK having adopted very different strategies to fight conoravirus, I developed this code to check trends between UK, Italy and Hubei. It is interesting that these three territories have similar population sizes but, until now, experienced the epidemics in different ways. Hubei got off-guard because it is the origin of the epidemics. Italy, together with South Korea and Iran got off-guard because they thought the coronavirus was somehow under control. UK might have done fewer mistakes so far and controlled the spread of the virus better and it has decided not to further contain the epidemics against WHO advice. Let’s see.

I synched the curved to a number of confirmed cases equal to 400. By chance, this is about the number of cases since we have data from Hubei, the day when Hubei went into lockdown, and a similar number when UK decided not to contain the virus.

Italy, just before and just after introduced first a local lockdown and then a national lockdown. Italy and Hubei seem to be on a similar trajectory of confirmed cases. For the UK is too early to say. We should keep in mind that confirmed cases depend on the methodologies of testing. Hubei’s and Italy’s health systems got overwhelmed, therefore it is possible at a certain point might have struggled to test the general population. The UK has decided to stop screening the general populations. Therefore, the reported deaths might be more realistic as numbers. At the time of writing, the JH dataset is one day behind, but we know that the UK is now in line with the other curves, and Italy is overshooting Hubei’s trajectory. Mortality rates are heavily affected by the reporting of confirmed cases. We will know the actual mortality rates only after epidemiologist will be able to do their statistical work retrospectively. More on this at the end of this post.

What about other countries? South Korea is interesting as they did not go into lock down but they also had a major outbreak. They were able to contain it by tracking those infected.

Assuming that Korea counted all covid-related deaths, their strategy was rewarded with a successful containment and lower deaths than other regions. Spain seems to be the EU country that will struggle next, let’ see…

Unfortunately, it seems that Spain is on the same trajectory compared to Italy and Hubei. But remember, Hubei succeed to contain the outbreak, which gives hopes. This and the experience in Korea is why WHO is still recommending to attempt containing the virus.

The same is true for France.

What about Germany?

For confirmed cases, Germany looks like on a similar trajectory. However, unless I did a mistake, the mortality rate seems very low. There are reports on the news that Germany considers as covid-related deaths only those patients who did not have other important related pathologies.

This, of course, would bias completely the curves we presented, but the situation in Germany might be not different form other countries. We’ll understand this in the future. Now a few comments on mortality rates. Initially, many of us were puzzled by the differences in mortalities between countries. There are several factors that influence these statistics: i) confirmed cases are underestimated in different ways in different countries because of testing capacity or policy. ii) covid-related deaths seem to be accounted similarly in many countries, except for Germany. iii) different countries have different demographics and iv) when a health system is strained both mortality might increase and confirmed cases decrease. All this considered, I just thought to give a reference for demographic adjustments.

I used mortality figures in Hubei and rest of China as to estimate the worst and best case scenario for an overwhelmed and a coping health system. The red and blue curves are these values adjusted by demographic differences in each country.

Then it seems that the high mortality in Italy is just demographics. Pay attention that these are cumulative statistics and, therefore, even if the situation improves massively like in Hubei, the mortality remains high because historically it was high. Thus, so far it looks like that only in Hubei and Italy the outbreaks arrived to the point to fully overwhelm the health systems. However, check the drift of the Italian curve, that is what might (hopefully not) happen in other countries that are on similar trajectory.

Keep in mind, I am no expert. I think however that there are two possibilities that explain this, and probably they both coexist. First, when ICU is overwhelmed, we rescue fewer people. Second, when a country is overwhelmed, there might be also fewer testing. So, plenty of limitations in this data (mortality rate data are no great, I am no expert, and several factors might explain the trends)… but at least there is some pattern that might indicate what is happening.

To conclude. Every country can still do what Hubei did. Not my work, but WHO’s. We need to protect the most vulnerable waiting for the vaccines and drugs that WILL come. Take care and find ways to keep positive and help people around you!

Coronavirus – getting ready in a lab

I am publishing here the recommendations I circulated to my colleagues, as this might help others to formulate their strategies or me to receive suggestions on how to improve. At the bottom of the post I also share my opinion about the situation, just to explain why we are taking action. Disclosure: I am no expert in this area, therefore I analysed data just to form my own opinion and to organize our work. Please check institutional guidance and reports.

Dear all,

      While we can still hope that no major disruption will occur, it is increasingly likely that the epidemic will not stop any longer. What is concerning, from a logistics point of view, is that this might last for several months as the responses of the public authorities will focus, rightly, in slowing down the epidemics. I would like to invite you to observe some basic rules, but also reflect on issues you might have not considered:

  1. The most important thing is to address the upcoming months with a scientific mind and no panic. Please follow the indications provided by the NHS, WHO, and the University.  While I do not doubt that all of us already wash their hands! Please do so also when you come in from outside, something we might usually not do.
  2. I am happy for you to work from home when you can. Most of you will have to carry out experiments, but I am happy for you to cluster reading and to write in specific days and to work from home. Would you need access to your computers from home, just organize this with IT but the Unit will provide appropriate IT arrangements soon.
  3. Some experiments could be rather expensive. Please let me know when this is happening so we could plan them properly. I would like not to delay important work though, so we might take some risks (on funds not on safety) but we could manage these risks proactively. For example ensuring that very expensive steps are executed in the shortest period of time and with sufficient people being aware of the experiment. For example, we have several commercial and in house developed cell lines that have not been archived yet.
  4. I can foresee two situations where we need to help each other. First, the case where a single individual will self isolate and they need help to store, throw materials, or shut down a microscope. I think Slack will suffice, but we should have also a ‘buddy system’. For example, if I started an experiment on a microscope that would last two days, I can inform someone else who would have the expertise to safely terminate the experiment.
  5. The second case is a bit more extreme but not really unlikely as other university campuses around the world have been closed. The Unit will soon provide specific guidance. Please think about which element would be critical, for example we will have to shut lasers down, air compressors to avoid them running out of oil etc.
  6. Do consider if you travel, even just within the UK, you might get stuck somewhere. Please check the University policies that are updated daily. I will not recommend specific actions related to personal trips, except to comply with public health guidelines and to think about the possible consequences to get stuck at home in one or another town.
  7. There is no indication – at the moment – that we will experience disruption to the supply chain. However, this might happen. Have a thought if we will run out of some consumables in a couple of weeks and perhaps order now.
  8. Also, very important. There are people coming from areas that are quarantined. Unless you are sure they are breaching rules, be supportive and do not make too many jokes. Some people are more sensitive than others.

To conclude, please do not allow the situation to make you anxious or too worried. For the general population, the main issue in not health but arranging life around likely restrictions of movement to permit the NHS to cope with the extra workload. For us, provided we will put first safety of ourselves, colleagues and family, we have opportunity to keep reasonably productive even in this situation simply organizing.

Feel free to propose ideas or to have a chat with me in private if you have any specific concern.

My opinion on the situation and on what is happening

I am growing of the opinion that the Italian situation is happening only earlier than in other European countries, not that is a special situation. Spain, France and Germany might be already on that path (10-14 days of delay compared to others). UK is probably an extra week late, meaning that by the beginning of April, or earlier, we will experience similar disruptions we are observing in Italy (hopefully not). Also, I had a look at mortality rates. Once taken into account the demographic and that in Italy we are experiencing a situation similar to Hubei (health systems overwhelmed) and not to the rest of China (managed containment of the disease), the stats of Italy do not seem odd to me any longer.

At this point, all other European countries will experience the same unless they enact strong preventive measures. To me it seems governments in Europe and USA have preferred to shield economy first rather than people, or they are simply incompetent, to then get caught off-guard and inflicting to the economy the same level of damage they would have got intervening earlier.
We can organize, minimize disruptions and deaths. Not eliminate them but we can do better we are doing. If only politicians would exert leadership, at local and national level, and – of course – people would comply with the indications…

Last word of caution. People might be complacent also thinking they have the best health systems. This is not the issue, the UK system will be as easily overwhelmed as the Italian one. In fact, there are fewer ICU beds in the UK than in most EU countries, including Italy. The point is to slow down the spread of the disease to keep our health system working within certain operational margins.

Bottom line. Am I writing to get your more worried or anxious? NO. The large majority of us will have minor health issues. However, the public health policies that will be necessary to minimize the negative impact on the NHS will cause major disruptions. Therefore, organize not by panic buying, but thinking ahead… how to work, look after family, etc, etc, when restrictions will be imposed.

Last thing. There is a tendency to minimize the situation as people dying is elderly affected by other pathologies. In Germany, it seems that they do not even consider those patients as CoV-related. The large majority of those people could have lived a much longer life, they are not (all) terminal patients. Moreover, with patients piling up in dedicated wards and ICU, everyone risks more because they will not receive adequate treatment, irrespective if they have been infected or not.

So… the apocalypse is not coming, but just the time to work together to get pass this.

Publishing: a business transaction

Until not so long time ago, desk-rejections (the editor decision not to proceed with peer-review of a submitted manuscript) or even rejections of a manuscript after peer-review with very little substance for that decision, could get me angry, at least in private. These emotions can motivate to do better, but most of the time – if we try too hard to get published in very selective journals – they can take a toll.

After speaking to several editors, I tried to focus on the fact that most of us (editors, authors, referees – sometimes the same people wearing different hats) are good and well-motivated people. That did not work. The sense of unfairness outranks that thought.

I tried to not care, and that did not work either. Until…

I believe that the large majority of scientists and editors do their job also for a clear vocation, to advance human knowledge for the benefit of society. For this reason, we often invest a lot more in our jobs that we should, emotionally and time-wise. This is why it might be difficult to have a detached view of what publishing is nowadays. Let’s make an effort together, watching the problem as a scientific one, analyze it, reducing its complexity to its components and mechanisms.

If you have a donkey and you want to sell the donkey, you go to the market. You might first go to a trader who pays very well as they have good contacts with wealthy farmers. However, they may not like your donkey even if you dropped the price. They do not like your donkey, why do you want to sell your donkey to them? Then you go to a different trader, they like your donkey and you settle for a fair price. But if your donkey is very old, you might come back to your farm with your old donkey. Perhaps you need the money and you get frustrated, maybe even angry, but which is the point? Business is business and the trader is simply doing their job.

Wait… what? D-D-D-Donkeys?

When you submit a paper to a journal, you try to initiate a business transaction. The editor is an expert trader, highly invested in their business and committed to maintaining their operations, legitimately, financially sustainable and profitable. The author trades-in two commodities, their manuscript and their reputation, and – additionally – pays a lump of money for the service. In return, the editor provides two commodities, their readership and their reputation, and – additionally – provides editorial services. I will perhaps elaborate in the future on the traded commodities and services, but for now, I keep this post to the bare essential.

The editor-trader first judges the quality of the product you want to trade-in. They are entitled to act discretionally applying their in-depth knowledge of their business to assess if they are about to initiate a potentially good deal. Can your donkey carry weight? Er, I mean, can your paper attract many citations and media coverage? If they do not want to do business with you, it is not a matter of fairness, even not of science, certainly nothing personal. It is the author’s responsibility to make their business pitch, and it is the editor’s responsibility to not lose good assets or not invest in bad ones.

If I read what I have just written ten years ago, I would have recoiled in disgust. Then I expect many scientists being horrified by what I have written and perhaps editors offended. I hope this is not the case, but if it happened, please let me clarify one point.

We (authors and editors) do what we do to advance human knowledge for the benefit of society. Boiling down everything to a mere business transaction feels perhaps bad. However, let’s keep in mind that scientific publishing is business. If it has to be or not, it is the subject of a different post and to the analysis of the nature of the commodities and services we trade.

For now, I just wished to share with you the trick I use to cope with the stress of rejections, particularly desk-rejections. That part of our job is just a business transaction. This thought helps me a bit more than anything else I tried before.

Lost in translation (dogma and science)

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.

Don’t fret about it, just get it (a FRET primer – Part I)

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.

Figure 1. Energy flows from a donor ‘D’ to an acceptor fluorophore by FRET. The ration of fluorescence emitted by the donor and acceptors (IDD and IDA) can be used to estimate how much energy is transferred from donor to the acceptor, quantity that is proportional to the distance of the two molecules.

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.

Figure 2. Cross-talks between donor and acceptor excitation. DE: direct excitation of the acceptor. SBT: spectral bleed-through of the donor emission into the acceptor channel.

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).

Figure 3. FLIM measure the time the donor-bucket needs tobe emptied, thus inferring the size of the second (FRET) hole.

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!


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).

Signor Tenente (a song against mafia)

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.

The coming year, talking about war

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

Riprodotto da Muzikum

L’anno che verra’ by Lucio Dalla

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

Riprodotto da Muzikum

Tackling cancer heterogeneity by live single-cell ‘systems biology’.

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 [7] 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 [14]. 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) [10]. 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 [10]. 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)

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