1
00:00:01,500 --> 00:00:08,640
Kills and is your fellow UPS and Cross College Oxford and professor in biological physics at university.

2
00:00:08,640 --> 00:00:14,970
Welcome to some crosscurrent shorts. Can you describe your way of doing biological physics?

3
00:00:14,970 --> 00:00:20,100
Sure. Let's consider what biological physics is in the first place.

4
00:00:20,100 --> 00:00:24,780
Biological physics is the application of experimental,

5
00:00:24,780 --> 00:00:35,040
computational and theoretical methods to study various biological systems that can be entire organisms or it can be biomolecules.

6
00:00:35,040 --> 00:00:42,030
So the approaches that we have in my love involve high end optical microscopy.

7
00:00:42,030 --> 00:00:52,200
That is combined with modelling and imaging image analysis, as well as conventional biochemistry that supports the biophysics work.

8
00:00:52,200 --> 00:00:57,330
And the organisms that we study are severe and viruses.

9
00:00:57,330 --> 00:01:02,660
We're very interested in the influenza virus and more recently, coronavirus.

10
00:01:02,660 --> 00:01:13,860
And the specifically the systems that we are studying are protein machines that are involved in gene expression and in DNA repair.

11
00:01:13,860 --> 00:01:24,660
That's why we actually call factionally our lab, the Gene Machines Group, because we'd like to work on machines that like to work on on DNA and RNA.

12
00:01:24,660 --> 00:01:33,540
And the main microscopy method that we employ is a single molecule, fluorescence, imaging.

13
00:01:33,540 --> 00:01:39,210
So we have ways of tagging our molecules of interest with fluorescent probes.

14
00:01:39,210 --> 00:01:44,910
And then we look at their at their motions in a way that that they can inform us about.

15
00:01:44,910 --> 00:01:49,890
There are mechanisms both in in cells and also in purified systems.

16
00:01:49,890 --> 00:01:55,020
So just to run quickly through the main approaches that we use in the group,

17
00:01:55,020 --> 00:02:01,170
we perform a lot of experiments with purified systems where we take a protein machine of our choice.

18
00:02:01,170 --> 00:02:07,230
For example, the machine that copies DNA into RNA, that's a protein called RNA polymers.

19
00:02:07,230 --> 00:02:16,410
We can put Fersen probes on the machinery or the DNA and then walks how it's being copied into RNA.

20
00:02:16,410 --> 00:02:25,170
And then by looking at the motions of the protein machine and how the DNA is is reconfigured during the process.

21
00:02:25,170 --> 00:02:26,670
We understand about the mechanism.

22
00:02:26,670 --> 00:02:34,890
Of course, when we do this, we also illuminate ways to stop the machinery of pathogenic versions of this protein machine.

23
00:02:34,890 --> 00:02:41,310
And if past few years we have been moving this single molecule methods inside the living cells.

24
00:02:41,310 --> 00:02:51,630
So we have ways, again, of labelling those protein machines of interest and then visualise how they function inside single bacterial cells.

25
00:02:51,630 --> 00:02:57,690
And because we have also very powerful ways of of localising these protein machines,

26
00:02:57,690 --> 00:03:05,430
we can use this this power in order to break the diffraction limited microscopy and get very detailed images of high

27
00:03:05,430 --> 00:03:13,630
resolution of this machinery and also other interesting structures in inside cells in order to be able to achieve these.

28
00:03:13,630 --> 00:03:14,040
So clearly,

29
00:03:14,040 --> 00:03:22,890
you have an interdisciplinary group that provides expertise both from the physical and biological side of the questions that we'd like to address.

30
00:03:22,890 --> 00:03:31,920
We also built our own microscopes and and also the software that's needed to operate them and analyse the images.

31
00:03:31,920 --> 00:03:37,770
And also we use the understanding of this machinery and the powerful microscopes in

32
00:03:37,770 --> 00:03:47,160
order to be able to come up with ultra sensitive assays that can detect pathogens,

33
00:03:47,160 --> 00:03:56,460
bacteria or viruses very sensitively. It's now mid-January 2021 and the UK is in its third COGAT Munchy locked down

34
00:03:56,460 --> 00:04:01,680
a covered 19 has changed since everybody to the four corners of the globe.

35
00:04:01,680 --> 00:04:08,630
How is it done so for you and your work? Well, obviously it has been a massive change, says for every one of us.

36
00:04:08,630 --> 00:04:12,850
An entire society has felt the brunt of the of the pandemic.

37
00:04:12,850 --> 00:04:17,160
But during the first leg down, we go back to March.

38
00:04:17,160 --> 00:04:27,630
Part of my team was the only group that was still performing experimental work in the physics department to work on a rapid test for SA v two,

39
00:04:27,630 --> 00:04:35,340
while the rest of my group was performing mainly computational work and developing software pursuing writing projects from home.

40
00:04:35,340 --> 00:04:40,080
Clearly, that was a very challenging time because I had to balance many responsibilities

41
00:04:40,080 --> 00:04:46,380
directing Colvard related research to keep every body engaged and upbeat,

42
00:04:46,380 --> 00:04:50,640
supporting people who were not working on the coffee research and were stuck at home

43
00:04:50,640 --> 00:04:58,620
while also completing my role as an examiner and also dealing with child care.

44
00:04:58,620 --> 00:05:06,480
Since I have toddler twins and having them at home during the first lookdown was was clearly a challenge.

45
00:05:06,480 --> 00:05:11,820
So I've been working from home for the past nine months and.

46
00:05:11,820 --> 00:05:21,930
I've given up my my office in order to lower the density in my group and allow more people to work and have some some some office space,

47
00:05:21,930 --> 00:05:30,390
additional office space. And of course, if I go back to July, when we're allowed to do experimental work again,

48
00:05:30,390 --> 00:05:36,900
we had to work hard to establish Calvet safe protocols, both as a lab and as a department.

49
00:05:36,900 --> 00:05:44,340
And the way we manage to operate quite effectively was by dividing the team into a green team and a red team,

50
00:05:44,340 --> 00:05:50,640
and therefore we're operating with 50 percent capacity. That still allowed us to have a good pace of research.

51
00:05:50,640 --> 00:06:00,900
But there's still many, many challenges present. For example, it's quite challenging to train new students that coming into the group,

52
00:06:00,900 --> 00:06:11,070
because clearly we need to to ensure that we we keep our social distancing protocols while training students to do experimental work.

53
00:06:11,070 --> 00:06:22,210
And it's hard to also assimilate them in the lab culture since the lab is operating in this kind of strange way and also teaching is done on line.

54
00:06:22,210 --> 00:06:31,290
And we had to adjust our procedures for for the examination period during the treaty term as well because of coffee

55
00:06:31,290 --> 00:06:40,440
and other types of impact that clearly they're not physical meetings anymore with colleagues within my my lab.

56
00:06:40,440 --> 00:06:46,650
So within physics and more broadly within the biophysics scientific community.

57
00:06:46,650 --> 00:06:53,010
So we actually had to cancel a wonderful conference that I was organising on the upside.

58
00:06:53,010 --> 00:07:05,400
We started attending more online conferences and given little talks remotely to places in the US or elsewhere in Europe.

59
00:07:05,400 --> 00:07:14,020
And without having to travel with so clearly as positive affecting, including the fact that there has negligible carbon footprint.

60
00:07:14,020 --> 00:07:17,670
This is one of this positive things that is coming out of the pandemic.

61
00:07:17,670 --> 00:07:27,240
And hopefully we will maintain the positive aspects and then go back to normal, having learnt some lessons along the way.

62
00:07:27,240 --> 00:07:32,970
Another lesson that we we have learnt, and that's something that I think has changed that during the pandemic,

63
00:07:32,970 --> 00:07:36,000
is that we see more international collaboration.

64
00:07:36,000 --> 00:07:43,380
People are just more willing to work, try to solve challenges that the pandemic has thrown in front of us.

65
00:07:43,380 --> 00:07:47,310
Also, we get a lot more attention from the press and the public,

66
00:07:47,310 --> 00:07:56,170
and we do the best we can in order to describe our work and to keep the public informed to the best of our knowledge.

67
00:07:56,170 --> 00:08:03,540
Your lab developed a rapid reverse test. Can you describe it and can you explain how it came to develop it?

68
00:08:03,540 --> 00:08:10,490
Yes, we have been working, as I mentioned earlier on, the replication mechanism.

69
00:08:10,490 --> 00:08:21,310
So for the influenza virus. So Saturdays work back in 2012 and more recently, we have been lowering ways to detect the flu virus more rapidly.

70
00:08:21,310 --> 00:08:31,980
In fact, in November 2019, just a few months before we learnt about the emergence of the new coronavirus.

71
00:08:31,980 --> 00:08:41,250
We had published a paper that was describing a method that was using calcium ions to bind tiny fragments or

72
00:08:41,250 --> 00:08:50,190
fluoresce and DNA on the particles of the flu virus and other envelope virus and to label them for recently very,

73
00:08:50,190 --> 00:08:59,070
very rapidly. So essentially you mix than flowers and DNA and calcium with the viral particles and they become almost instantly forests.

74
00:08:59,070 --> 00:09:02,580
And then you can detect them on fluorescence microscope.

75
00:09:02,580 --> 00:09:12,900
And by looking at the motion in solution or looking at their structure while they mobilise them on the surface,

76
00:09:12,900 --> 00:09:17,760
you get information that may allow you to identify the virus.

77
00:09:17,760 --> 00:09:23,640
So the timescales for labelling and imaging are basically just one to two minutes.

78
00:09:23,640 --> 00:09:32,340
So it is very rapid. So when the coronavirus emerged in China, we thought that our AC will work quite well.

79
00:09:32,340 --> 00:09:42,810
So with a new coronavirus and we had already some very exciting data on using images of the flu virus,

80
00:09:42,810 --> 00:09:49,740
along with the use of machine learning in order to identify different strains of flu virus.

81
00:09:49,740 --> 00:10:00,070
That was work that was done by a brilliant graduate student, Physick Senecal associate this in collaboration with a Royal Society fellow doctor.

82
00:10:00,070 --> 00:10:10,870
And we called Rob, who now is an assistant professor at work. And that gave us a lot of confidence that this this methodology will work.

83
00:10:10,870 --> 00:10:19,600
Also on the new coronavirus, so the important thing then was to be able to get the right samples in order to test the principle that

84
00:10:19,600 --> 00:10:28,240
post on using a coronavirus from from chickens that showed that this principle is valid for coronaviruses.

85
00:10:28,240 --> 00:10:38,350
And then in collaboration with an institute in Montpellier who had access to the new coronavirus and high containment facilities and microscopy,

86
00:10:38,350 --> 00:10:48,700
we showed that indeed our idea was correct and the method was working quite well in terms of the detection, at least on the new coronavirus.

87
00:10:48,700 --> 00:10:56,050
So what we did after that was to start working with colleagues in the hospitals on Rapid Fire Hospital,

88
00:10:56,050 --> 00:11:02,320
which expanded on the collaboration we had started on bacterial targets in,

89
00:11:02,320 --> 00:11:07,720
say, a few things a bit later about that that allows us to basically access locally

90
00:11:07,720 --> 00:11:12,610
to the virus and provided us with space where we could install the microscope.

91
00:11:12,610 --> 00:11:17,170
That allowed us to look at clean clinical symbols when we did this.

92
00:11:17,170 --> 00:11:26,440
We managed to show that, again, the principle works not only on lab grown coronaviruses, but also on clinical samples.

93
00:11:26,440 --> 00:11:34,920
So we have excellent accuracy when we look at individual particles of coronaviruses in clinical samples.

94
00:11:34,920 --> 00:11:40,000
And now what we're doing is to be able to analyse many,

95
00:11:40,000 --> 00:11:46,420
many clinical samples in order to establish the formal specificity and sensitivity of the assay and find ways

96
00:11:46,420 --> 00:11:58,000
to make it also more adaptable and more high throughput in order to be able to be used for mass testing.

97
00:11:58,000 --> 00:12:03,110
And the important feature of the methodology is its its speed.

98
00:12:03,110 --> 00:12:13,060
Essentially, it wanted two minutes. You can have a result that identifies whether somebody has the novel coronavirus or not.

99
00:12:13,060 --> 00:12:18,430
And it said and I say that this also quiet general and may be used for other viruses as well.

100
00:12:18,430 --> 00:12:27,190
So we feel that this will have a substantial impact in terms of detecting dangerous viruses.

101
00:12:27,190 --> 00:12:37,370
We'll see how much of an impact we'll have during this pandemic. But in any case, viruses will always be a some some visits and we should be prepared.

102
00:12:37,370 --> 00:12:42,320
And it will help with that. You said that this work is involve collaboration.

103
00:12:42,320 --> 00:12:46,520
Of course, the university Oxford. Way beyond the interest book.

104
00:12:46,520 --> 00:12:49,120
And you say some more about this. Sure.

105
00:12:49,120 --> 00:13:01,090
So the collaboration with the John Radcliffe Hospital and especially the lab of Derek Krook, I think both on called Stresser,

106
00:13:01,090 --> 00:13:10,840
Monique Anderson and Lee and Teto was very, very important without them would have been able to show the proof of concept with clinical samples.

107
00:13:10,840 --> 00:13:16,960
And we are extremely thankful for their support and their enthusiasm.

108
00:13:16,960 --> 00:13:25,030
Also, I should point out the help from the Micron imaging you needs that is based in biochemistry.

109
00:13:25,030 --> 00:13:28,360
Ellen Davis is a person who was heading this activity,

110
00:13:28,360 --> 00:13:36,400
who actually support us by lending us a microscope that we could using in the short term for for about a month.

111
00:13:36,400 --> 00:13:39,010
And that's clearly accelerate our timescale.

112
00:13:39,010 --> 00:13:45,850
So that was an important contribution, our colleagues at Montpellier who actually contacted us after that.

113
00:13:45,850 --> 00:13:51,340
So some of our efforts discussed the on on Twitter and then offered their help.

114
00:13:51,340 --> 00:14:01,210
I was actually very heartwarming to see. And also colleagues per bright institute that studies animal viruses helped us in this effort.

115
00:14:01,210 --> 00:14:01,520
And also,

116
00:14:01,520 --> 00:14:10,630
I should say that there was support and various levels through our colleagues who are participating in the University Wide Calvet update group.

117
00:14:10,630 --> 00:14:17,230
So it's other groups that have been doing Colvard related research since essentially meet the

118
00:14:17,230 --> 00:14:24,580
January that the set of colleagues involves people who are working on developing the vaccine.

119
00:14:24,580 --> 00:14:31,030
So the head of the Oxford Vaccine Group and my colleague Andy Poulard and colleagues who work on structural biology,

120
00:14:31,030 --> 00:14:38,230
on immune responses, on track and trace. So we had excellent discussions there that helped us shape our project as well.

121
00:14:38,230 --> 00:14:47,140
So this is a list of individuals and institutions that were very helpful in our efforts agency currently working well.

122
00:14:47,140 --> 00:14:57,130
We are continuing our efforts on rapid coronavirus testing, but we have started our noncurrent virus experimental working in late July.

123
00:14:57,130 --> 00:15:06,670
So on that frome's. We are finishing some very exciting work on the recent discovery of the detail mechanism by which the RNA polymers.

124
00:15:06,670 --> 00:15:15,710
So the machine that copies DNA to do RNA. So. At the heart of gene expression, and we've done this for the machinery of the bacteria.

125
00:15:15,710 --> 00:15:24,470
And then we understood the series of motion motions that take place in order to open their DNA to start the copying of the gene.

126
00:15:24,470 --> 00:15:33,530
So very exciting. And we are wrapping it up for publication. We are also continuing the work to study the function of this chinnery,

127
00:15:33,530 --> 00:15:41,180
along with the injury repair machinery by watching motions of single molecules inside living bacterial cells.

128
00:15:41,180 --> 00:15:45,060
So that's ongoing work things.

129
00:15:45,060 --> 00:15:52,340
It's a very good stage. And then another project that we're ramping up right now has to do with the rapid detection

130
00:15:52,340 --> 00:15:58,190
of antibiotic resistance in pathogenic bacteria that are found in clinical samples.

131
00:15:58,190 --> 00:16:05,420
And this sexually with the same team that we developed the rapid test for for coronavirus.

132
00:16:05,420 --> 00:16:13,520
This particular detection has to do with bacteria, relies on single cell imaging and also involves advanced image analysis.

133
00:16:13,520 --> 00:16:19,820
That includes also the machine learning aspect of the use of artificial intelligence.

134
00:16:19,820 --> 00:16:28,280
The idea here is to be able to take a clinical specimen and to either analyse it directly or through

135
00:16:28,280 --> 00:16:35,080
some minimal purification and then using imaging of individual cells as they are exposed different.

136
00:16:35,080 --> 00:16:39,890
The biotics to be able to tell whether these cells are antibiotic resistant or not

137
00:16:39,890 --> 00:16:44,090
and also get some information about what type of bacteria we have in the sample.

138
00:16:44,090 --> 00:16:53,150
So that's a collaboration, again, with the colleagues in the John Radcliffe Hospital and also a colleague in their locker at the Big Data Institute.

139
00:16:53,150 --> 00:17:00,160
And has this work has received a generous support by the Oxford Martin School.

140
00:17:00,160 --> 00:17:04,160
So the project is starting basically in a few weeks.

141
00:17:04,160 --> 00:17:07,910
The funded project. But we have exciting preliminary data already.

142
00:17:07,910 --> 00:17:12,500
And another front of these quite important is the establishment of a new

143
00:17:12,500 --> 00:17:19,220
interdisciplinary institutes at Oxford that will open in a matter of a few months.

144
00:17:19,220 --> 00:17:24,620
And this is an institute is recently received support by the Kavli Foundation.

145
00:17:24,620 --> 00:17:32,570
And this institute is focussing on using physical approaches in order to study biological mechanisms in living cells.

146
00:17:32,570 --> 00:17:37,970
So we will actually be moving to a building has been completed obsessively.

147
00:17:37,970 --> 00:17:47,480
Yes. As we speak. And therefore, will house many scientists that are working on on the physical approaches to deciding biological mechanisms.

148
00:17:47,480 --> 00:17:53,990
Presumably, a new interdisciplinary institute is going to be taking your research into the future.

149
00:17:53,990 --> 00:18:01,290
Could you say it's a bit more about that? Yes, absolutely. As I mentioned then, as you can see from the work that we discuss,

150
00:18:01,290 --> 00:18:07,640
are the rapid detection of pathogenic bacteria, rapid addiction or coronavirus and other viruses.

151
00:18:07,640 --> 00:18:16,670
We are doing more translational work than in the past because we feel that it's important to make put

152
00:18:16,670 --> 00:18:22,970
our methodologies and competences towards a more immediate impact for the society and public health.

153
00:18:22,970 --> 00:18:29,630
So we'd really like to make a contribution and to focus our efforts in the

154
00:18:29,630 --> 00:18:35,780
method development or putting our better mechanistic understanding to words,

155
00:18:35,780 --> 00:18:43,220
helping rapid detection and therefore helping the clinicians to to ensure better public health.

156
00:18:43,220 --> 00:18:52,730
Also, I see that we will be doing more computational and image analysis work in the future because it's now in many of our projects,

157
00:18:52,730 --> 00:18:58,760
extremely easy to collect. A very large number of data and images.

158
00:18:58,760 --> 00:19:07,070
So it is to take. We have even commercialised microscopes that make this process of data acquisition quite, quite easy.

159
00:19:07,070 --> 00:19:14,270
And there are more tools to do image analysis and data analysis and therefore provide

160
00:19:14,270 --> 00:19:21,200
information that allow us to drive new experiments or new interpretations of existing data.

161
00:19:21,200 --> 00:19:29,810
It's also something that can be done from from Holeman, as we don't know when exactly the end of the pandemic will will be.

162
00:19:29,810 --> 00:19:37,970
Nobody knows the ability to do quite a bit of work from from home without requiring extensive experiments is quite attractive.

163
00:19:37,970 --> 00:19:45,620
Also, I see ourselves doing more working in living cells versus doing work and purifying systems because they're more

164
00:19:45,620 --> 00:19:53,240
biologically relevant and involve all the components associated with the growth and the maintenance of a of an organism,

165
00:19:53,240 --> 00:20:03,860
sir. And the last thing I see that is emerging is that we will be involved in more collaborative efforts either internally within the new institutes,

166
00:20:03,860 --> 00:20:09,900
but also internationally. And one of the reasons for this is that the problems that we're trying to.

167
00:20:09,900 --> 00:20:13,920
Face are quite difficult that require a multidisciplinary approach.

168
00:20:13,920 --> 00:20:18,720
And despite our lab being fairly multidisciplinary,

169
00:20:18,720 --> 00:20:28,050
clearly will be more more effective and efficient if we tackle it with with colleagues and experts in in fields that we perhaps novices.

170
00:20:28,050 --> 00:20:34,080
And the fact that we will have a new institute that will have this wonderful environment of groups

171
00:20:34,080 --> 00:20:40,220
working on different approaches will make this more more easy to achieve and more enjoyable.

172
00:20:40,220 --> 00:20:49,808
Really pillages. Thank you so much for taking the time to talk today.