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Changed around line 216: Thankfully, many people are experimenting with better ways for sharing science.
- Scientific essays are intriguing because they are free to be everything papers are not: opinionated, informal, and dare I say, fun to read. Instead of every sentence being assembled by committee to avoid a reviewer’s wrath, essays offer an opportunity for unfettered scientific expression. We even already have a platform for distributing them: Twitter! Over the past few years, Twitter is where I’ve discovered my favorite scientific essays, some of which I’ll link here, here, and here. It doesn’t escape my attention that none of these were written by scientists in academia. While I’d love for this to change overnight, I realize that academics don’t have many tangible incentives to write. So for now, here’s my more concrete suggestion: PhD students should write part of their dissertation as a scientific essay.
+ Scientific essays are intriguing because they are free to be everything papers are not: opinionated, informal, and dare I say, fun to read. Instead of every sentence being assembled by committee to avoid a reviewer’s wrath, essays offer an opportunity for unfettered scientific expression. We even already have a platform for distributing them: Twitter! Over the past few years, Twitter is where I’ve discovered my favorite scientific essays, some of which I’ll link here, here, and here. It doesn’t escape my attention that none of these were written by scientists in academia. While I’d love for this to change overnight, I realize that academics don’t have many tangible incentives to write. So for now, here’s my more concrete suggestion: *PhD students should write part of their dissertation as a scientific essay*.
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Changed around line 212: Here’s what I came up with: the ideal purpose of sharing science is to stimula
+ https://www.arcadia.science/ Arcadia
+ https://research.arcadiascience.com/reimagining-scientific-publishing open notebooks on PubPeer
+
+ http://jck.bio/learning-representations-of-life/ here
+ match 0
+ https://ldeming.posthaven.com/sequencing-is-the-new-microscope here
+ match 1
+ https://jsomers.net/i-should-have-loved-biology/ here
+ match 2
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Changed around line 203: Happy exploring,
- Postscript: why write this?
+ # Postscript: why write this?
+
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Changed around line 196: While live imaging followed by fixed in situ measurements might reveal how past
- Why should we care about the future? I recently watched a video speculating on when the last human would live. The main premise was that if we manage to avoid cataclysmic events that wipe out all of humanity (a big if, but stay with me), humans will conservatively survive for at least a million years. And if we solve global warming and space travel and other future issues, we may survive many, many orders of magnitude beyond that. But given that we’ve existed for only 200 thousand years, it’s exceedingly likely we live right at the start of human history rather than towards the end. On the cosmic scale of human civilization, we are still discovering the foundational technologies.
+ Why should we care about the future? I recently watched a video speculating on when the last human would live. The main premise was that if we manage to avoid cataclysmic events that wipe out all of humanity (a big if, but stay with me), humans will conservatively survive for at least a million years. And if we solve global warming and space travel and other future issues, we may survive many, many orders of magnitude beyond that. But given that we’ve existed for only 200 thousand years, it’s exceedingly likely we live right at the start of human history rather than towards the end. *On the cosmic scale of human civilization, we are still discovering the foundational technologies*.
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Changed around line 197: While live imaging followed by fixed in situ measurements might reveal how past
+ https://youtu.be/LEENEFaVUzU when the last human would live
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Changed around line 192: multimodal.png
- Early days
+ # Early days
+
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+ Early days
+ By synthesizing the very best of microscopy and sequencing, the in situ technologies of the future will let us perceive biology at unprecedented resolution. And the new discoveries they enable will propel us to create even more advanced technologies that make the future a brighter place.
+
+ Why should we care about the future? I recently watched a video speculating on when the last human would live. The main premise was that if we manage to avoid cataclysmic events that wipe out all of humanity (a big if, but stay with me), humans will conservatively survive for at least a million years. And if we solve global warming and space travel and other future issues, we may survive many, many orders of magnitude beyond that. But given that we’ve existed for only 200 thousand years, it’s exceedingly likely we live right at the start of human history rather than towards the end. On the cosmic scale of human civilization, we are still discovering the foundational technologies.
+
+ Happy exploring,
+
+ ZC
+
+ Postscript: why write this?
+ My original plan for my PhD dissertation was to staple all of my papers together. I know many people disagree with this practice, but this has always seemed patronizing to me. If I’ve published actual articles, why should I waste time re-writing them in a form nobody will read? Then some of my collaborators’ experiments failed, and during my newfound free time, I thought a lot about what it really means to share your science.
+
+ Here’s what I came up with: the ideal purpose of sharing science is to stimulate discussion, inspire new ideas, and in the best cases, shift the collective consciousness.
+
+ Though Science Twitter is known for its disagreements, it’s safe to say we all agree on one thing: our current system for sharing science does not live up to our ideals. We could discuss the issues with scientific journals all day, but the problem with actual papers is they are both longer and emptier than we would like. Modern papers are filled with pages and pages of supplementary figures to appease cantankerous reviewers, while devoid of the thought-provoking speculations and musings once found in older literature. Most PhD dissertations, on the other hand, are little more than glorified lab notebooks, written more for obsessive completeness than for readability to fellow scientists.
+
+ Thankfully, many people are experimenting with better ways for sharing science. Preprints let us share our work faster and theoretically open up peer review to the public, but are still largely beholden to the formatting and style whims of the traditional publishing system. Arcadia is piloting open notebooks on PubPeer, but for now, this is more at the institutional level rather than a choice an individual can make. Personally, I believe the long-term solution is not a single approach, but a buffet of options for every circumstance. Here, I’d like to advocate for a format that may appeal more to those in academia: the scientific essay.
+
+ Scientific essays are intriguing because they are free to be everything papers are not: opinionated, informal, and dare I say, fun to read. Instead of every sentence being assembled by committee to avoid a reviewer’s wrath, essays offer an opportunity for unfettered scientific expression. We even already have a platform for distributing them: Twitter! Over the past few years, Twitter is where I’ve discovered my favorite scientific essays, some of which I’ll link here, here, and here. It doesn’t escape my attention that none of these were written by scientists in academia. While I’d love for this to change overnight, I realize that academics don’t have many tangible incentives to write. So for now, here’s my more concrete suggestion: PhD students should write part of their dissertation as a scientific essay.
+
+ In the spirit of being the change I want to see, I have shared my own attempt here. It was certainly harder than I thought it would be! After years of writing papers, it was difficult to deprogram the jargon from my brain and write in a more accessible way. I also worry that people will think I’m boring or stupid or pretentious for believing my thoughts are worth sharing. But in the end, my goal was to write the essay I would’ve wanted to read as a 1st year grad student, and I feel I’ve put forth my best effort.
+
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Changed around line 184: Unfortunately for temporal measurements, one technology we won’t see any time
+ https://www.nature.com/articles/s41592-018-0111-2 label-free microscopy
+
+ multimodal.png
+ caption Ounkomol et al. Nature Methods (2018)
+
+ While live imaging followed by fixed in situ measurements might reveal how past behavior affects cell state, we also want to learn how cell state predicts future behavior. Given that we can’t perform live imaging of a cell after fixed measurements, how can we accomplish this? One possibility is to train deep learning models that can foresee the future. Several years ago, Buggenthin and colleagues demonstrated that live imaging can be used to predict a stem cell’s lineage prior to the appearance of known molecular markers. In theory, you could train similar models for any cellular system with a heterogenous response, such as drug resistance or epigenetic reprogramming, and then perform in situ measurements at an early time point to identify which molecular states are most commonly associated with each predicted fate.
+ https://www.nature.com/articles/nmeth.4182 Buggenthin and colleagues
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Changed around line 178: Another reason to be excited about expansion microscopy is that it enables in si
+ https://www.cell.com/cell-systems/fulltext/S2405-4712(17)30132-1 method that involves sorting cells into microfluidic wells for live imaging, followed by single-cell sequencing.
+ https://www.science.org/doi/10.1126/science.abl5981 a paper combining calcium imaging to record electrical activity and RNA FISH to identify neuronal subtypes
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Changed around line 167: Expansion microscopy, which we introduced earlier as an example of creative inve
+ alon.png
+ caption Alon*, Goodwin*, Sinha*, Wassie*, Chen* et al. Science (2021)
+
+ Another reason to be excited about expansion microscopy is that it enables in situ technologies to be applied to thick 3D samples. Right now, nearly all spatial technologies are designed for thinly-sliced tissue sections that contain a single layer of cells (10-20 micron thickness). While 2D spatial information is better than nothing, biology exists in 3D, and ideally our measurements should reflect that. Because expansion involves embedding your sample in a 3D hydrogel, we can take advantage of tissue clearing techniques to remove debris and improve enzymatic diffusion. These techniques unlock spatial profiling in thick samples where 3D context is critically important, such as embryos, organoids, or neural synapses. In the future, it may even be possible to measure all the molecular interactions within expanded intact organisms. But even if we reach this point, these measurements still represent a single snapshot in time – how can we expand our capabilities to measure the dynamic nature of cellular processes?
+ https://www.nature.com/articles/nprot.2014.123 tissue clearing techniques
+ https://elifesciences.org/articles/46249 expanded intact organisms
+
+
+ # Everything, everywhere all at once
+
+ Now that we’ve considered bringing in situ measurements into 3D, let’s explore the 4th dimension: time. For all of sequencing’s strengths, perhaps its biggest limitation is its destructive nature, precluding dynamic measurements of cells. Most technologies that claim to capture temporal information from sequencing actually take individual measurements of cells at varied time points, in contrast to repeatedly measuring a single cell over time. One exception is this method that involves sorting cells into microfluidic wells for live imaging, followed by single-cell sequencing. While technically impressive, this approach is limited in throughput, and each cell is measured in isolation as opposed to its native tissue context.
+
+ Unfortunately for temporal measurements, one technology we won’t see any time soon is the ability to sequence in live cells*. Although this would seem to limit us to the combination of live imaging and sequencing described above, I believe in situ technology has the potential to make this approach far more versatile as follows: Instead of sorting cells into individual wells, we can simply perform live imaging on all of them at once, followed by fixation and multiplexed in situ measurements. Because the latter preserves spatial structure, it is then trivial to link the final cell locations from the live imaging with the corresponding fixed measurements. This framework has already been demonstrated in a paper combining calcium imaging to record electrical activity and RNA FISH to identify neuronal subtypes, offering a generalizable template for linking live cell phenotypes to fixed in situ measurements.
+
+ Since this approach to temporal measurements requires both live imaging and in situ technologies, we must also develop better methods for time-lapse imaging of multiple markers at once. One promising solution is a computational imputation technique known as label-free microscopy. To set up this technique, you first capture a basic imaging modality (e.g. brightfield, phase contrast) in parallel with immunostaining for cellular structures such as the nuclear lamina or mitochondria. Next, you train deep learning models to create mappings from the basic modality to each immunostain, and then lastly, perform continuous live imaging for the basic modality and apply your models to predict the immunostain at every time point. Though label-free microscopy is still at the proof-of-principle stage, it offers the promise of one day allowing you to predict the dynamics of any protein for “free” from basic live imaging data.
+
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Changed around line 157: Why is it so important to measure everything at once, as opposed to performing m
+ # Accelerating expansion
+
+ Measuring molecular interactions is not as straightforward as it sounds. While multi-omic sequencing approaches can read out several layers of regulation in a cell, they struggle to distinguish between direct interactions and correlative associations. Is the methylation of a regulatory region causing lower expression of a nearby gene? Or alternatively, perhaps they are both downstream products of the same pathway, but do not interact with each other. While most sequencing approaches cannot make this distinction, in situ technologies can identify direct interactions based on 3D distance, given sufficient spatial resolution.
+
+ All microscopy methods have a fundamental tradeoff between spatial resolution, plex, and throughput. For instance, electron microscopy has the highest spatial resolution (~0.1 nm), but minimum plex (only one “color”). Super-resolution methods like STORM that use randomly-blinking fluorophores to distinguish crowded molecules enable high resolution fluorescence imaging, but are low-throughput because samples must be imaged repeatedly. Though advancements in physics may gradually minimize these tradeoffs, what paths are available to us to increase the spatial resolution of in situ technologies by several orders of magnitude?
+
+ Expansion microscopy, which we introduced earlier as an example of creative inversion, is a form of super-resolution imaging compatible with in situ technologies. With a method called ExSeq (Expansion Sequencing), the Boyden lab showed that expansion can drastically improve both the yield and spatial resolution of in situ RNA sequencing. One reason I am particularly optimistic about expansion is its spatial resolution is infinitely scalable in theory. By varying the chemical compositions of your hydrogel, you can increase the amount it expands in water (i.e. its expansion factor) from 4-fold all the way up to 24-fold. Although larger expansion factors exacerbate gel handling difficulty, these capabilities offer a theoretical path forward for one day capturing molecular interactions at biology’s tiniest scales. This approach is also currently practical as well: in the ExSeq paper, even basic 4-fold expansion allowed RNA molecules to be localized to nanoscale compartments such as dendritic spines.
+ https://www.science.org/doi/10.1126/science.aax2656 ExSeq
+ https://www.biorxiv.org/content/10.1101/2022.04.04.486901v1 24-fold
+
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Changed around line 149: Though the breadth of modalities continues to rise, certain experimental obstacl
+ https://www.cell.com/cell/fulltext/S0092-8674(20)30870-9 CODEX
+
+ goltsev.png
+
+ Why is it so important to measure everything at once, as opposed to performing many single modality measurements? One good reason is experimental throughput. Instead of limiting ourselves to mostly normal and well-characterized disease states, we want to explore the full perturbation landscape with genome-scale CRISPR methods or vast libraries of pharmacological compounds. We’d also like to associate the molecular state of a cell and perturbations with image-based phenotypes, such as cell division defects or cell-to-cell interactions. With single modality measurements, we’d have to perform millions of experiments to access every combination of modality, perturbation, and phenotype, but with molecular multiplexing, we can constrain our search space to the latter two. There’s also a second, compounding advantage to measuring everything at once: by identifying each molecule’s neighbors in 3D space, we can also measure all molecular interactions.
+ https://www.cell.com/cell/fulltext/S0092-8674(22)00597-9 genome-scale CRISPR methods
+ https://www.biorxiv.org/content/10.1101/2021.11.28.470116v1 cell division effects
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Changed around line 135: Collectively, these capabilities would allow us to visualize everything happenin
+ # Mo molecules, mo problems
+
+ How might we eventually identify all of the molecules within cells? Examining current technological trends might offer clues. In 2019, Nature Methods annointed single-cell multi-omics as its Method of the Year, highlighting an emerging class of assays used to make several types of measurements from the same cell. A few prominent examples include:
+
+ - SHARE-seq and 10x multiome (RNA and chromatin accessibility)
+ - CITE-seq (RNA and select surface proteins)
+ - Paired-Tag (RNA and a single histone modification)
+ - Genotyping of Transcriptomes (RNA and select genotypes)
+ - DOGMA-seq (RNA, chromatin accessibility, select surface proteins, and mitochondrial mutations!)
+
+ Though the breadth of modalities continues to rise, certain experimental obstacles may disrupt this upward trend. In particular, simultaneously measuring many proteins and epigenetic marks may be difficult because these methods rely on oligo-conjugated antibodies, which notoriously have specificity issues. Epigenetic marks are extra challenging because diploid cells only have two sets of chromosomes. In this case, the unique strength of sequencing – reading out nucleotide order – is simultaneously a weakness, since our ability to read out multiple modalities may ultimately be constrained by the finite quantity of DNA you can extract from a cell.
+
+ Luckily for us, in situ technologies don’t rely on DNA extraction. In a recent paper, Takei and colleagues showed it is possible to jointly measure DNA, RNA, proteins, and epigenetic marks in situ in the same sample. In this particular approach, epigenetic measurements were read out through microscopy instead of sequencing, which yielded lower genomic resolution, but enabled the authors to discover nuclear zones defined by unique combinations of histone marks. Meanwhile, in situ proteomics methods such as CODEX can sidestep issues of antibody specificity using successive rounds of immunofluorescence to image over 50 proteins, rather than trying to sequence all of them at once. Though in situ technologies come with certain trade offs, I’d argue they offer a much clearer path to measuring everything inside a cell than single-cell multi-omic approaches.
+ https://www.nature.com/articles/s41586-020-03126-2 Takei and colleagues
+
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Changed around line 131: Afeyan explains that once you take this leap, you can work backwards to discover
+ Collectively, these capabilities would allow us to visualize everything happening within cells in real time, ideally without any negative effects. This technology would be invaluable to preventing disease, elongating healthy lifespan, and perhaps even augmenting ourselves to explore the vast reaches of the universe. Slightly ridiculous, right? But the point of taking a leap of faith is to consider the impossible and translate that vision into tangible first steps you can take today. Here are some roadmaps towards the in situ technologies of the future.
+
+ [Despite my best efforts, the next three sections get fairly technical. If you’re not super familiar with genomics, I won’t be offended if you skim or skip them.]
+
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Changed around line 125: To imagine a new technology is to envision the future. Noubar Afeyan, founder of
+ Afeyan explains that once you take this leap, you can work backwards to discover what you should currently be doing. Let’s try this together: Imagine you are in charge of designing the microscope-sequencer of the future, a device vastly beyond the limits of our current technology. For now, let’s put aside how it will work, and focus on what it should be able to do. Personally, I can think of three fundamental capabilities:
+
+ 1. Molecular resolution: be able to identify every molecule (DNA, epigenetics, RNA, proteins, etc.)
+ 2. Spatial resolution: be able to pinpoint the exact 3D location of every molecule to identify interactions
+ 3. Temporal resolution: be able to do 1 and 2 at every point in time
+
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Changed around line 123: In this paper, you will learn how combining sequencing and imaging in a single t
+ > Almost by definition, breakthroughs in their embryonic stages defy existing theories, principles, and bounds of experience. As such, they should be considered leaps of faith. So to foster emergent discovery in your organization, you need to make it acceptable to consider the seemingly impossible.
+
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Changed around line 121: In this paper, you will learn how combining sequencing and imaging in a single t
+ https://hbr.org/2021/09/what-evolution-can-teach-us-about-innovation emergent discovery
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Changed around line 116: In situ sequencing was first demonstrated for RNA by Je Lee, Evan Daughtery, and
+ In this paper, you will learn how combining sequencing and imaging in a single technology allows us to see what your genome looks like at the very first stages of life. But for the sake of being nonredundant, here I will skip ahead to discuss what in situ technologies might look like in the future.
+
+ # Take a leap of faith
+
+ To imagine a new technology is to envision the future. Noubar Afeyan, founder of Flagship Pioneering (the venture firm behind Moderna), has thought at length on how to systematically create new technologies using a process called emergent discovery. The whole piece is worth a read, but my favorite part is the idea that innovation requires taking a leap of faith to break free from the status quo:
+
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Changed around line 114: In situ sequencing was first demonstrated for RNA by Je Lee, Evan Daughtery, and
+ spatial.png
+
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Changed around line 106: So back to our essential question: how do you both image and sequence a cell? It
+ // note: do the rest of italics from below here later.
+
+ The term in situ means in its original place or position. To perform in situ sequencing, we first use chemical fixatives to keep DNA and RNA in its original place instead of extracting it from cells. After amplification, we then mimic the sequencing chemistry that occurs within an Illumina flowcell, only here, it all occurs within cells. This lets us measure both the sequence (ACGT) and the 3D spatial position (xyz) of each molecule.
+
+ In situ sequencing was first demonstrated for RNA by Je Lee, Evan Daughtery, and George Church with FISSEQ in 2014. But subsequent progress was slow, since sequencing inside the cluttered environment of a cell is both time consuming and technically challenging. And while 30 rounds is sufficient for measuring RNA, it isn’t nearly enough for other modalities, such as the genome. If you’re interested in the creative leaps it took to solve these challenges, check out our method for in situ genome sequencing.
+ https://www.science.org/doi/10.1126/science.1250212 FISSEQ
+ https://www.science.org/doi/10.1126/science.aay3446 in situ genome sequencing.
+
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Changed around line 104: expansion.png
+ insitu.png
+
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Changed around line 102: One of Ed’s favorite strategies for creativity is inversion: if you’re stuck
+ So back to our essential question: how do you both image and sequence a cell? It turns out that inversion also works here, but first, we need to understand how sequencing works. The first step is to extract and amplify DNA. Next, you load the amplified DNA onto a flowcell, which is a surface containing billions of evenly-spaced nanowells. The flowcell then goes into the sequencer, where it undergoes successive rounds of four color imaging, as seen below. In this video, each spot corresponds to an amplified DNA molecule, the four colors correspond to fluorescently-labeled DNA bases (ACGT), and each frame corresponds to a round of sequencing. Finally, this video is used to read out the order of nucleotides for each DNA molecule. Now, did you catch the key point? A DNA sequencer is actually a simplified microscope! (Inversion alert!) *So what if we could use a traditional microscope to perform sequencing inside of cells*? This idea is known as *in situ* sequencing.
+
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