In rapidly evolving fields such as life sciences and biotechnology, understanding the intricacies of career growth and technological advancement is crucial. That’s why we’re bringing you direct insights from industry experts in our ‘Becoming’ series which spotlights the transformative journeys of professionals in these fast-paced spaces. Our aim is to inspire the full spectrum of talent by sharing the unique career pathways that have led individuals from all walks of life to carve out careers at the intersection of science and technology.

Whether you’re interested in making the shift from academia to industry, looking to climb the career ladder or just keen to hear first-hand experiences of cutting edge technologies in the life sciences and biotech arenas, these stories offer valuable lessons in passion, perseverance, and purpose.

I specialize in connecting life sciences companies with top-tier talent and recently had the opportunity to interview Sunny Trivedi, a senior scientist in spatial biology. Sunny shares his journey from India to the U.S. and delves into the technical aspects of spatial biology that fascinate him.

The conversation reveals a lot about the practical, ethical, and motivational aspects of working in life sciences; from the ‘immediate impact’ one can have when working in the industry as a major advantage of moving from academia, the dual-sided nature of patents, the role of mentors, and the personal attributes needed to thrive in the field, such as passion and adaptability.

Watch the 30-minute interview to take in an enlightening conversation that traverses borders and delves deep into molecules, or read the full transcript below:




Starting off with really, it’d be great to hear a little bit about a brief overview yourself, your background, your university courses, and where you started.

My name is Sunny, I’m originally from India and I did my master’s over there and then I came over to the US. It was always clear to me that I wanted to come to the states and do a PhD. And I came here, started my PhD in the city of Maryland, Baltimore County. I stayed there for 5 to 6 years, and I learned quite a bit about things and now I’m working in a special biology field.


How did you find how did you find that transition moving over to the US?

It really can depend on the personality, I guess, for some people it can be nerve-wracking, but for me, I am quite a bit of a risk taker and I really embrace and love the change. So, I was over the moon. I was very excited to move here and start to work alongside with like, some of the best minds in the field, right? that is since back in India, but at the same time here that is starting research. The technologies are here, and you get to work on that first hand, and besides that it’s always exciting for me to move to a new country, explore the new culture and meet new people.


I’m glad that it’s been a good transition. Obviously, you’ve been in the US quite a while now, so fully settled in. It would be good to hear a little bit about some of technologies you’ve worked with and some of the work experiences that you’ve had.

So back in India, I started my master’s. So that’s where I learned quite a bit about proteins, how they work, how they structurally they are related to each other and how they interact with each other and how these tiny molecular interactions, they impact on our organism level. So even like a small protein, which you can’t even see by a naked eye right, interaction of these 2 teams, that can change on the full path physiological structure of a human being and how it can impact on a macro level. I think that fascinated me a lot. And so that’s where I got into the protein, I learned on the protein techniques. So that includes how to create the recombinant routines, not in computing how to express them in the material and sales and after expressing them how to purify them or how they how to understand the interaction between each other and how or what kind of impact does that have. As I said, macro level and I learned I have in my career, I’ve learned more about, I would say techniques rather than technology because I feel like techniques are much more important than technology because it keeps you very connected with the signs at a molecular level. So, you understand what’s happening in the calls in real time. Whereas technology will make your life easier. So, technology can always be like the final output, but if you understand the techniques which goes behind that, I think that helps you a lot. So that’s what I focused for throughout my career until I finish my PhD. And now I feel like I have a decent grasp of how things happen at a seller level. I know am venturing into the technology space where I’m trying to understand that.


What are some of the applications that you that you’ve worked with or that you’ve researched?

That’s a great question. And uh, I will talk more about some of the applications that I did after that I worked with a which is after my PhD. But for this part I think it’s mostly understanding the things behind the signalling pathways that are involved. For example, this whole migration is regulated by a 10 to 15 different signalling pathways that we know of. And so, some of them is, let’s say signalling. So signalling is one of those signalling pathways, which is heavily involved, I would say in consumer datasets. So, it kind of mimic where let’s just for our very main idea that I understand. For example, this is a tumour. It detaches from one place in our body and then it goes and migrate to some other place and then it in fact, the other place.

So, these are the pathways which are all involved in the cancel meta, and we can understand metadata. And so that is just one kind of output of the federal physiological feature that could be it could be involved in information, the recovery and many other signalling and live at the log.

This is mostly we are targeting and look at the groupings. So, there is not any sequencing involved in this part. However, after this I started my journey at census. We that’s where we looked at a lot of single sale Alice detection which kind of can be read out by either microscope or the sequencing.


What ways can it then benefit the wider community?

That’s a great question. So, this is I guess whatever I did during my, that was more about just basic research. It’s the goal is to understand the pathway and not have any like, direct translation, or relationship. However, I ship this idea where it’s a special biology, right? You understand how self and means and the routine they interact with each other and on a special pain field. But for example, if I want to look at one protein, it will take me about a week to thing them, look at them and then study this, right? If you want to look at multiple points that is. There was no high group solution at that time. That’s where I started all my journey at a census. They can detect more than 100 routines on a single-issue section at the same time. And so basically, what you do is you take the slides with the section on it. You put all the bodies on it and then you put it under the scope, and it will image for like a day or day and a half. And then it will directly spread out an image which has 100 pointed at the same time. That saves a lot of time and throughput, right? And that’s what they were doing, and I joined the team to do the exact same thing for me. And so, then we wanted to detect both protein and on the issue section. And then I was given a chance to lead the team where we were trying to have this technology already where we can detect both protein and aren’t on the same tissue section.

So, if we can have a technology which can detect 114 and 100 aren’t on the same tissue section, I think that sees a lot of time in different fields of pilot. One of the examples being clinical studies, right? In clinical studies, a patient comes to you. Patient asks that, hey I’m facing these kinds of foundations. What do you think is going on with this thing. So, we can take the section from that person, and then we can study that. Hey, let’s look at this panel of 100 to 10 and 100 in and see which protein is or expressing which ran as or expressing or under expressing and from that we can make a very good diagnosis that hey this person can be suffering from this kind of situation and let’s have our targeted or drug trap for that person. So, it has multiple  magnitude of detection, which can be improved, and we can have a very nice prognosis and diagnosis for cancer any of the information, any of the men logic relativity is.

It’s not just the medicine, right? You can always work with prognosis where, for example, your I’m sure like, your parents are healthy, but for example let’s say somebody’s parents are predisposed to a certain condition, right? And you may have a fear that, hey, will I have this or not? You can always go to a doctor and then have this whole panel run on your sample and see if there is a predisposition for this dish or not. And then you can have or change your life still according to that.


And for the image that you mentioned, it goes for about one and a half days. Is that being the molecules moving quite slowly? Is that why you’re doing it over such a long period of time and then the final image you kind of speed up to make it look faster? Is that how it works?

That’s a great question. So, I think that’s the limitation of the dies that you can detect a sample with, for example. For our visible length. The want that is the only 13 number of targets that you can detect at the same time. You can use one of the dice, which is in the range of for 18 names, which is like, close to JP than 5 fifty, which is near the two channel and then a bit of forward. So, you can detect maybe let’s say 5 molecules at the same time because then you run out of all the wave. And what you must do is you have to deposit this molecule onto the slide. It takes some time to hybridize for them. Then you image them. So, it takes some time to image the whole process. And after the imaging is done, you must wash it off and then introduce the next batch. So, in the first batch you introduce you detected 5 targets in the next batch. You again, do 5 and five, it’s kind of slick detection and through which you can have more than 200 targets detected, whether it’s routine or not. So, it’s more like you’re doing a few at a time and then sort of putting those videos almost on top of each other to get a better idea.

If you were to do this manually, I think it takes a lot of time because like 5 targets one week. But if it’s an automation, you can easily cut down that time by 95% and then it’s just all automation. So, you can just put the sample on the instrument and then be gone. And then after 2 days, the whole thing is ready for you. And automation is very expensive. These instruments, if I must put like a ballpark number, I will say they do cost around $3 – 400,000. So almost like the whole setup is gonna be like around from half a million. So not everybody can afford that, but it’s like for industrial purposes and for clinical purposes or for some research is cut on it may be possible.

I think it makes it difficult for university students as well because like I see, for example, the sequences are very expensive. So very often they do the sample prep for the sequencing and then they send it off to a call. Which is quite challenging. Because like, you’ve got a lot of these companies that want people to come in with experience using sequences and all these bits of experience that they kind of price and them out from doing. I mean, one of the main causes of that is really a lot of sorts of the patterns that are on the market. There are some big players out there that have patterns on some of the larger sequences that make it harder for the smaller stamps to reduce that price and really come in as competition.


What are your, sort of thoughts on. Do you agree with a lot of these patterns? Or do you think maybe they should be a bit shorter or like maybe even changing the rules around them?

I have pros and cons for when it comes to cadence, I guess for on both sides, for example. I think the cadence right now are close 15 to 20 years for a single company when you find out that which is quite a long time. So, I feel like it could be shorter close to 5 or 10 years because that gives a company enough time to make back all the money that they have invested in the research and then also profit from them and be the leader in the market because they have all the sample data to kind of do machine learning from them, right? That should be good enough time 5 to 10 years for a feed in to last not more than that. But also, then there is a count to that. For example, if there is a shorter feeding, that discourages other competitors who innovate because if there is a shorter feet and they were like, okay, it’s only 10 years after 10 years, it’s open, we can have the exact same thing and then we can be in the market, where if the Peter is longer that discourages from other people to rely on them, but also come out with their own technology because they don’t want to be waiting for 20 years. So, they would rather come up with their own technology, which will compete with them and that will innovate and push the scientific boundaries a little bit further. So, I think that is just good and bad for both the sides. And we just must be mindful and in the I guess the urgency if it directly help or innovate, patients’ medical journey.


What is it that you love most about working in, sort of science and by technology?

I think it has always been pushing the specific boundaries and having this kind of major impact. So, when I was growing up, I always wanted to be when I was a kid. I always wanted to be a doctor. I was like, okay, hey, how can I have a very good impact on a society? And so being a doctor was what came to my mind when I was 10 / 11 or how as I grew, I understand a lot more about the importance of a broader impact. For example, if it’s a doctor, it’s going to be hey you are sharing that patient, but if you are assigned this you can work on these different ideas. And if you can have a breakthrough, let’s say drug development or a technology development that can impact, not just one or 2 patients, it can impact the whole community and having that chance of impacting the broader community in a more positive way. It inspires me a lot and it keeps me going always.


What would you say has been the biggest impact on your work?

Like if you talk about mentors, I think my mentor, she was one of the best scientific minds that I’ve ever come across and it’s not just the scientific mind, but the determination that it’s kind of infectious. Because she works in and out all day and she always has the smile on her face as if she enjoys this. This is it’s like I’m from India. I love cricket, right? It’s like, hard playing cricket all the time. So being in a field where she enjoys this all the time. So, she’s a month beauty. Beautiful kids she has pets. She’s she was the grader director at that time and now she’s the lean of the department. She’s a wife, she cooks me. She prefers how home cooked meals. And even after that, she is giving her a plus game in the research, and she manages the live which has multiple students or must write the grants. And even when she when the invite her to our house. So, I would see like a gig so of those getting formed or she would net she would do like some sort of painting. So, she even after having this many responsibilities, she would have time for these kinds of things and I get really, really impressed and I always call a superwoman because we always cry about hey, I don’t have enough time to do this and enjoy the life, but she does it all and always has a very big smile on our face. So that only happens when you’re passionate about what you’re doing, and it does not tie you.

I think we always try and name to achieve and drive like that as well. I think trick, I think outside curriculum and activities as well is just so important more like mental health wise as well. Especially in such a fast-paced environment in small buyer tech is a lot of hours. There is a lot of work there. And a lot of pressure and stress.


What is really some advice that you’d like to give to sort of other post grad that were in your position?

Yeah. That’s a good question. So, I feel like I have sort of 2 different pathways to this. So, first of all is many people feel like going from academics to industry is a tableau for some of them. For example, you’re working in an academic lab. Students always feel that their mentors would not like it. If they go from an academic to industry because they do want to have their own family in the academic fields be there. We have the strategy going on. However, this is only I feel like it’s in the minds of students. Only professors do not feel that way. They want that students and candidate to flourish in any way that’s possible. So, I would say do not be afraid of going into the industry field from academic. It’s always well. It’s sought after well thought after and then it’s always having that translation impact to the society. That is well worth it. In the second one is yeah, having a fear of failing or a very fast-paced environment of the industry compared to the academics. Where you are used to taking your own time and not diving deep into the time. So, I’d say like it’s a learning curve on. Nobody is expecting for us to excel in the first month or 6 months after you join the industry if that’s the case.

Just focus on the end goal. And that helps you succeed a lot and it’s much better than academic from my sense because you get to be in touch with North collaborative people. Great minds in the field and it’s that you get to be introduced with so many new techniques and fields that you will never be able to do during your PhD because PhD or even after PhD when you are doing a post or a very niche very focused field. Whereas in the industry you get to explore so many different areas.



We look forward to bring you more inspiring career stories over the coming months. In the meantime, check out our Women Revolutionizing series.