Cohesin is a trimeric complex containing a pair of SMC proteins (Smc1 and Smc3) whose ATPase domains at the end of long coiled coils (CC) are interconnected by Scc1. During interphase, it organizes chromosomal DNA topology by extruding loops in a manner dependent on Scc1’s association with two large hook-shaped proteins called SA (yeast: Scc3) and Nipbl (Scc2). The latter’s replacement by Pds5 recruits Wapl, which induces release from chromatin via a process requiring dissociation of Scc1’s N-terminal domain (NTD) from Smc3. If blocked by Esco (Eco)-mediated Smc3 acetylation, cohesin containing Pds5 merely maintains pre-existing loops, but a third fate occurs during DNA replication, when Pds5-containing cohesin associates with Sororin and forms structures that hold sister DNAs together. How Wapl induces and Sororin blocks release has hitherto remained mysterious. In the 20 years since their discovery, not a single testable hypothesis has been proposed as to their role. Here, AlphaFold 2 (AF) three-dimensional protein structure predictions lead us to propose formation of a quarternary complex between Wapl, SA, Pds5, and Scc1’s NTD, in which the latter is juxtaposed with (and subsequently sequestered by) a highly conserved cleft within Wapl’s C-terminal domain. AF also reveals how Scc1’s dissociation from Smc3 arises from a distortion of Smc3’s CC induced by engagement of SMC ATPase domains, how Esco acetyl transferases are recruited to Smc3 by Pds5, and how Sororin prevents release by binding to the Smc3/Scc1 interface. Our hypotheses explain the phenotypes of numerous existing mutations and are highly testable. Cohesin is a multi-subunit complex responsible for determining much of the topology of chromosomal DNA during interphase as well as sister chromatid cohesion . At its In the presence of ATP, the Smc1 and Smc3 ATPase domains associate with each other, thereby sandwiching a pair of ATP molecules between them . CrucialIn addition to holding sister chromatids together in post-replicative cells, cohesin is capable of extruding long loops of DNA . This loCohesin’s initial association with chromosomes as well as active LE are mediated by Smc-kleisin rings occupied by Scc2/Nipbl and Scc3/SA . LE is hPost-replicative cells have three major types of cohesin complexes: those holding sisters together (sister chromatid cohesion), those actively extruding loops, and those whose LE has been arrested by CTCF and which hold together convergent CTCF sites at TAD boundaries. Crucially, all three are susceptible to the action of a fourth regulatory subunit called Wapl , which iBecause its maintenance does not require Scc2/Nipbl, sister chromatid cohesion is thought to be a passive structure that does not require continual ATP hydrolysis. Once generated during replication, cohesion must also be shielded from Wapl. This protection depends on Smc3 acetylation, and in animal and plant cells on cohesin’s association during S phase with a protein called Sororin . When ceCohesion is thought to be mediated through the topological entrapment of sister DNAs by individual tripartite Smc-kleisin rings , either X-ray crystallography and electron cryomicroscopy (cryo-EM) have revealed many details of the topology of Smc1/Smc3 heterodimers , how theWe report here a complementary approach to in vitro biochemistry and experimental structural biology, namely in silico 3D protein structure prediction by AlphaFold 2 (AF) , based oSMC1(L1129V) or SMC1(D1164E) that suppress eco1Δ lethality  of Brn1’s NTD . Because it also occurs in the presence of ADP and BeFse heads . This rase heads . This rahttps://doi.org/10.6084/m9.figshare.22567318.v2. To address whether head engagement is compatible with association of the N- and C-terminal domains of Scc1 with Smc3 and Smc1 ATPase heads, respectively, we asked AF to predict the structures of Smc1 and Smc3 heads associated with coiled coils extending up to but not including their joints in the presence of a truncated Scc1 polypeptide in which its conserved NTD and CTDs were joined by a GS  linker  linker , f2. AF ) linker , as was Thermotoga maritima hinge associated with short coiled coils  to which Scc1’s NTD is normally associated and that this distortion may cause its dissociation. A corollary is that maintaining the association of Scc1’s NTD with the Smc3 neck should prevent either joint junction or even head engagement. Consistent with this hypothesis, the addition of Scc1’s NTD , f16 buteco1 mutants in yeast , which is much closer to Wapl’s conserved CTD, is present and highly conserved in that location among all fungi  within a highly conserved surface  that sits underneath the pronounced ‘nose’ of Scc3/SA. This binding must be of physiological importance as K to E mutations abrogate Wapl-dependent RA in vivo, both in yeast  and in cin yeast . However mutants , implyinll fungi . Unlike revisiae . Thus, tS. cerevisiae , and a trough situated between a pair of HEAT repeats located within Pds5’s C-terminal half, at the point where its longitudinal axis starts to curve , which is conserved in numerous ascomycetes including to curve , f19. Beto curve , f20 andto curve , f21, asto curve , f22, weto curve  predict  its CTD . However its CTD , f20. In its CTD , f23, bu its CTD , f19, AFS. cerevisiae TYxxxR[T/S]ΦL motif with full-length Pds5 (f19A), it predicted, in five out of five models, interaction of the equivalent motif from Neurospora crassa with Pds5’s APDAP loop , as did (using a different Wapl truncation) its [M/Φ]xxYG[K/R] motif (f19D), in this case in a manner resembling a crystal structure of the equivalent complex from humans (PDB: 5HDT)  . Given tIn contrast to yeast Pds5, AF did predict with high confidence in five out of five models a rigid body contact between a complex between yeast Scc3 and Scc1, and the first (α1) and second (α2) α helices within Wapl’s CTD , f24, whA very similar if not identical interaction was predicted between the human SA2:Scc1 complex and a fragment of human Wapl containing its entire N-terminal extension as well as half of its CTD (a prediction with full-length SA2 and human Wapl would have been close or over AF’s size limit)  , f25. IS. cerevisiae Wapl(D272G) was in fact isolated as a spontaneous suppressor of eco1 lethality in yeast. The AF prediction for the human proteins explains the greater conservation of the surface of Wapl’s α1 facing SA2 as well as the more N-terminal V639, which also contacts SA2. Crucially, the AF model explains why individual mutation of E653, D656, D657, or Y660 within the CTD’s α1 helix abrogates RA, as does mutation of L676 or K684 in its α2 helix  is highly conserved , insects (Drosophila melanogaster), and a basal fungus, namely the chytrid Spizellomyces punctatus , but did so with high confidence in the top two ranked models created in the presence of the part of Wapl’s CTD that it predicts to bind both SA and Pds5 , f27. Thunctatus  within hunctatus , right. S. cerevisiae (f32A). Further analysis of the way Wapl interacts with Pds5 in S. punctatus revealed a possible explanation. When the former’s CTD was attached to N-terminal residues containing its TYxxxR[T/S]ΦL motif, AF predicted that Pds5’s NTD formed a rigid body complex with the CTD, whose conformation was identical to that observed in the canonical Pds5:SA/Scc3:Wapl rigid body ternary complex (S. cerevisiae (f19C) and N. crassa (f19B). This raised the possibility that an interaction between Wapl’s TYxxxR[T/S]ΦL motif and Pds5’s APDAP loop might actually facilitate formation of ternary complexes and that this might explain why the TYxxxR[T/S]ΦL motif is essential for RA in S. cerevisiae. We therefore repeated the AF predictions for the previously described S. cerevisiae Pds5, Scc3, and Wapl fragments (f32A) but in this case included sequences containing Wapl’s TYxxxR[T/S]ΦL motif. Remarkably, the top three ranked models now predicted with high confidence a rigid body ternary complex  and the basidiomycete Ustilago maydis (f33B). As in S. cerevisiae, formation of the canonical Pds5:Scc3:Wapl ternary complex in Neurospora was also strictly dependent on inclusion of its TYxxxR[T/S]ΦL motif (compare f33A and f33C). These observations not only suggest that formation of a canonical Pds5:SA/Scc3:Wapl rigid body ternary complex is conserved in fungi as well as animals and plants, but also that its formation may be an essential feature of RA, because deleting the TYxxxR[T/S]ΦL motif necessary for its in silico prediction in S. cerevisiae (f32) abolishes RA in vivo  revealed a very similar looking quarternary complex .Having realized that Pds5 organizes the spatial position of Scc1’s NTD, we superimposed the human Pds5:Scc1N model , f41. ReN), and the N-terminal straight section of Pds5 whose spine binds Scc1. All five models predicted with high confidence a ternary complex between Wapl, Pds5, and Scc1N (N’s α-1 and Wapl were not predicted with high confidence (f42), did not look convincing, and more seriously, differed in their details between models.As mentioned, in humans and most other metazoa, as well as in many fungi  and plannd Scc1N , f42. Ino to α-1, is sandwiched between two long antiparallel helices that form a pronounced nose at the C-terminal end of Wapl and a second pair that cross over at 45o. AF predicts similar interactions for D. melanogaster (f34B), Caenorhabditis elegans (f34C), and for A. thaliana (f34D). Moreover, a slightly different but clearly related interaction was also predicted for N. crassa (f34E). Remarkably, only modest changes in the conformation of Scc1’s NTD are required to accommodate binding of its α-1 and α0 helices to Wapl’s CTD in this manner, principally a slight bending of α3 to avoid a clash with the pair of Wapl CTD helices that cross its nose. As a consequence, α3 lies on top of and almost parallel with Wapl’s nose and this kind of alignment is presumably why AF failed to predict Wapl’s interaction with Scc1’s α-1 and α0 when Scc1’s NTD is connected to Pds5 when the latter is part of a ternary complex with Wapl  that, according to the PAE and pLDDT plots . It is important to note that the mutations did not affect any of the residues that actually contact Wapl in these models. Being in α3 itself, which does not associate with Wapl, the mutations merely affect how the two N-terminal helices associate with α3 . Because very similar rigid body ternary complexes are formed between Pds5, Scc3, and Wapl in plants as well as fungi  helices by Wapl’s CTD is of physiological importance, then the Wapl residues involved in sequestration should be conserved. This consideration cannot be applied to Scc1’s NTD as it has multiple other functions. Highly conserved  human Wapl surface residues involved in the interaction include N939 and N990, D979 and E1117, which could interact with Scc1[R11], L983 (which could interact with Scc1[L8]), A1112 and H1115 (which could interact with Scc1[W23]), M1116 (whose sulfur atom could interact simultaneously with Wapl[F1165] and Scc1[W18]), and lastly M1166 (which could interact with Scc1[Y3]). Remarkably, several of these residues have been mutated and shown to abrogate Wapl’s RA activity in vivo, namely Wapl(D979K), M1116A, E1117K, and F1165A . In concas fungi , the N-tN’s α-1  has previously been highlighted by Chatterjee and co-workers , a feature that might also apply to S. cerevisiae.The cleft within Wapl’s CTD putatively bound by Scc1-workers , who desC) and the pronounced protrusion within the middle of Pds5 (Pds5P) .Our AF predictions suggested that formation of canonical rigid body Pds5:Scc3:Wapl ternary complexes in ascomycetes depends on a juxtaposition of Wapl’s CTD and Pds5’s NTD facilitated by Wapl’s TYxxxR[T/S]ΦL motif , which i (Pds5P) , f54A. C (Pds5P)  that abrth Scc3C . The remC face involved in the interaction appears unique to ascomycetes, it has a potentially important implication for how canonical rigid body Pds5:Scc3:Wapl ternary complexes are generated in S. cerevisiae. This emerges from the fact that the Scc3C:Pds5P interaction aligns the axes of the two proteins in parallel and thereby juxtaposes their N-termini, albeit modestly spaced apart  a complex with Scc3C bound to Pds5P, Wapl’s CTD bound to Scc3’s CES, a close juxtaposition of the latter with Pds5’s NTD, and interaction of Wapl’s TYxxxR[T/S]ΦL with Pds5’s APDAP loop . Because it was unclear whether or not Smc3’s KKD loop binds another protein or merely binds DNA, we focused on what proteins might bind Scc3N. Because the predictions described above excluded both Wapl (f24) and Pds5 (f54A), we tested whether Scc3N might bind Smc3’s ATPase domain. Remarkably, AF predicted with high confidence, in the top four ranked models, an interaction between the face of Scc3N containing D189 and E202 and the part of Smc3’s ATPase containing K112 and K113 (N and Smc3’s KKD loop. Because Scc3(E202K) suppresses the lethality caused by unregulated RA caused by Smc3 double mutants as well as eco1Δ  and K113 by Eco1 in yeast blocks release in a manner that does not require Sororin, nor why release is abrogated by mutations of Smc3’s KKD loop, Smc3 and its surroundings Smc3 or by Scc3(D189E) and E202K , situateand K113 , f56A. Nand K113 . Again, as eco1Δ , we can N, including D189 and E202, is highly conserved in fungi and AF predicted a similar interaction between Scc3N and Smc3’s ATPase domain in Candida albicans (f57A) but not, strangely, in N. crassa (f57B). D189 and E202 are not conserved in metazoa and AF also failed to detect any interaction between SA/Scc3N and Smc3’s ATPase domain in humans (f57C), where the physiological role of Smc3(K105) and K106 acetylation has never been rigorously investigated.The face of Scc3N and Smc3’s ATPase when the former was bound by Wapl’s CTD  and K113 would be in an ideal position to contribute to such DNA binding.Finally, AF predicted a very similar interaction between Scc3pl’s CTD , f56B anpl’s CTD , f56C, ireaction . HoweverS. cerevisiae (f67) and in five out of five models with Candida glabrata (f68), AF predicts with high confidence an interaction between yeast Eco1 and Smc3’s ATPase with Scc1’s NTD bound to the coiled coils emerging from it (Xenopus laevis (PDB: 5N22) (Acetylation of Smc3(K112) and K113 by Eco1 is essential for blocking RA in G2 cells, a process dependent on Pds5 . How doe from it . ImportaB: 5N22)  (f69) (FB: 5N22) , left. TB: 5N22) . AF predPassage through S phase is necessary for acetylation by Eco1  and EscoS. cerevisiae Pds5’s most C-terminal HEAT repeats  to chromatin in a pre-RC dependent manner , separated by 10 residues from a so-called YSR motif (consensus [M/W]S[K/Q]KVRRSYSRL). Functional studies indicate that the Sororin box is essential, while the YSR and FGF motifs are not, at least when the protein is overexpressed . BecauseX. laevis (f85), Danio rerio (zebrafish) (f86), D. melanogaster (f87), Tribolium casaneum (red flour beetle) (f88), A. thaliana (f89), and even the ascomycete Aspergillus fumigatus (f90). The similarity of the predictions is remarkable given that the Sororin orthologs of vertebrates, insects, plants, and fungi have very little sequence homology and even exhibit considerable variation between their Sororin boxes , we considered whether it might instead bind to the Smc3:Scc1 interface. Remarkably, AF predicted with high confidence in two out of five human models interaction with an Smc3 ATPase domain whose coiled coils were bound by Scc1’s NTD , f84. A in boxes . In contnogaster , f91, A.thaliana , f92, anfumigata , f93. Infumigata , f94. ThN’s long α3 helix, thereby preventing sequestration occurring even before the NTD has dissociated from Smc3. Perhaps more importantly, by binding over Scc1’s N-terminal helices and simultaneously binding Smc3, Sororin could actually stabilize the interaction between Scc1’s NTD and the Smc3 neck, thereby thwarting any subsequent sequestration of its N-terminal helices by Wapl. Such stabilization might also prevent the distortions in Smc3’s neck produced by simultaneous head engagement and joint junction, thereby preventing such an event. Despite these insights, AF predictions provided no clues with regards to the role of Smc3 acetylation.According to all of the AF models, Sororin’s C-terminal α helix straddles Scc1’s most N-terminal helix and the loop connecting its fourth helix to the long α helix that forms a three-helical bundle with the Smc3 coiled coil . MeanwhiN sequestration cleft), when part of the canonical ternary RA complex with SA and Pds5, then the [K/Q]KVRRSYSRL motif would be free to bind to Pds5’s APDAP, which is brought into close proximity to the aforementioned cleft in Wapl’s CTD by ternary complex formation. In other words, if Sororin’s [S/T]ΦFGFExL and [M/W]S[K/Q]KVRRSYSRL motifs are to bind cohesin simultaneously, the short linker connecting them will constrain the distance allowed between their binding sites. If the [S/T]ΦFGFExL motif binds to Pds5, its partner must also bind to an adjacent site on Pds5  and usually evaluates interactions either pairwise or among a very limited number of constituents and not as in vivo where proteins must compete with a large fraction of the proteome, as well as with other macromolecules. We contend that many if not most of the hypotheses described in this article are plausible, novel, in some cases provocative, and above all explanatory, and therefore deserve communication before validation using orthogonal approaches.C and Pds5P and between Scc3N and Smc3’s KKD loop. Combining cross-linking with a more limited mutational analysis is probably therefore the most economical way of proceeding, but even this is will be a considerable undertaking as all substitutions should be made within endogenous genes.What then might these be? At the top of the list must be renewed attempts to visualize the predicted complexes by cryo-EM alongside the use of bifunctional thiol specific reagents to crosslink both in vitro and in vivo cysteine pairs inserted into the predicted interfaces . MutatioThough our experience is anecdotal, the modeling that we undertook for this analysis was sufficiently extensive that it is worth sharing the impressions gained into the strengths and weaknesses of AF as well as what may transpire to be best practice. First and foremost is the issue of the optimum scale for embarking on a prediction. On the one hand, AF’s ability to detect patterns between specific domains clearly diminishes as the size and number of components increase. The combinatorial space increases with the square of the number of residues provided in the query sequence(s). On the other hand, predicted interactions clearly have greater value if AF manages to predict them within the context of intact and therefore possibly large proteins or protein assemblies, whose size approaches the limits of what is currently practical or even possible given GPU memory limitations. It is our experience that initial forays should be performed while making as few assumptions as possible as to the nature of the interactions and therefore giving AF as large a landscape as is practically possible. Nevertheless, following up interactions revealed in this manner also has value. It is also clear that there is probably no relationship between the confidence of AF predictions and any actual affinity between proteins, as would be expected given the AF algorithm. If biology demands a weak interaction and natural selection has delivered this, AF clearly excels in picking up the resulting patterns and in this regard it provides an approach that is complementary to conventional biochemistry and structural biology with which weak interactions are sometimes difficult to investigate. As a consequence, many of the interactions detected by AF may be challenging to pursue using these techniques and combining experimental structural work with the sort of cross-linking techniques described above may prove useful in this regard. Another important lesson, which may prove to be of general validity, is that complexes such as cohesin should not be regarded as conventional solids, which imply a lack of constituent mobility or as liquids, which would require massively larger stoichiometries. They should instead be considered as promiscuous molecular communities made up by a large set of potential interactions with a great heterogeneity of on and off rates and extensive competition between different constituents, many of which have multiple possible partners and multiple binding interfaces. Some of the interactions are between relatively rigid domains but many are between short motifs embedded in very long intrinsically disordered loops and extensions. Understanding the physical dynamics of such communities as well as the mechanisms underlying the switches between states will be a formidable challenge. In the case of cohesin, both loop extruding and cohesive forms must be capable of switching between forms that associate stably with chromatin fibers to ones that dissociate, and a competition for shared binding sites between factors promoting release or its inhibition presumably create bi-stable switches.In summary, our AF predictions have yielded several hypotheses for how cohesin’s association with chromatin is regulated. These include:The role of cohesin’s hinge in effecting a junction between the joints within Smc1 and Smc3’s coiled coils. When combined with ATP-driven head engagement in the absence of Scc2/Nipbl, but in the presence of Pds5, this junction is predicted to create a distortion of the coiled coil to which the Scc1’s NTD normally binds and thereby triggers its dissociation.The proposal that Wapl’s CTD forms a ternary complex with the pair of cohesin’s HAWK subunits previously implicated in release, namely SA/Scc3 and Pds5. Upon dissociation from Smc3, Scc1 is predicted to bind to Pds5 in a manner that places Scc1’s short N-terminal helices in close proximity to a highly conserved site within Wapl’s CTD when part of the ternary complex with SA and Pds5. The subsequent unfolding of Scc1’s α-1 and α0 helices from the long α3 helix that normally associates with Smc3’s neck then leads to their sequestration by this part of Wapl. We propose that sequestration in this manner is the chief function of Wapl and that it serves to keep the exit gate open long enough for DNA to pass out of the cohesin ring.C:Pds5P interaction is required for subsequent formation of the canonical one.The finding that in yeast formation of the canonical ternary complex needed to deliver Scc1’s N-terminal helices to Wapl may be a multi-stage process during which an alternative ternary complex involving the Scc3S. cerevisiae where it binds the N-terminal face of Scc3.The first insight into how Smc3’s KKD loop facilitates release, at least in An understanding of how the acetyl transferases that block release and compete with Wapl for binding cohesin’s HAWK subunits bind to their substrate and how this is facilitated during S phase by simultaneous binding to Pds5 and to the Mcm helicase.The first insight into how Sororin is recruited to cohesin and how it hinders Wapl-mediated release, through the association of a conserved C-terminal α helix with cohesin’s exit gate, namely the junction of Scc1’s NTD with Smc3’s neck.The discovery that Scc2/Nipbl and SA/Scc3 interact with each other via an association involving the latter’s highly conserved nose, for which there has hitherto been no explanation. Whether this association has a role during LE or in the entrapment of DNA inside the cohesin ring remains to be addressed.N is probably the most remarkable as it is based not on a single AF prediction but on an elaborate concatenation of numerous models, producing a quarternary complex involving Wapl, Pds5, SA/Scc3, and Scc1, in which Scc1’s N-terminal α helices are positioned next to a pair of crucial sites within Wapl’s CTD. Key observations underpinning this notion include the conservation of Wapl’s putative Scc1N binding site, the existence of mutations within it that abrogate release in vivo both in yeast and in mammalian cells, the prediction by AF that Pds5, SA/Scc3, and Wapl form a highly specific ternary complex in animal, plant, and fungal lineages, that Scc1N’s association with the spine of Pds5’s NTD places the formers’ α-1 and α0 helices next to the putative binding site within Wapl’s CTD, that formation of the ternary complex in fungi depends on a motif within Wapl’s unstructured N-terminal sequences which binds Pds5’s APDAP loop and is essential for RA in S. cerevisiae, AF predictions for the interaction between Scc1N and Wapl that appear conserved among a very wide variety of eukaryotes, and lastly on the fact that dissociation of Scc1N from Smc3 depends on Wapl in vivo in S. cerevisiae and that RA is abrogated by their co-translation. Whether like a jigsaw puzzle, our hypothesis proves to be the sole solution to these explicanda will nevertheless require numerous types of wet experiments.Of these, the notion that a key function of Wapl’s CTD is to bind α-1 and α0 from Scc1Last but not least, it should be pointed out that due to the nature of AF, none of our models incorporate DNA and therefore shed no direct insight into how sequestration of Scc1N by Wapl actually facilitates cohesin’s dissociation from chromosomes in vivo.https://doi.org/10.6084/m9.figshare.22567525.v1) were created using PyMOL v2.5 . Each subunit was drawn in all figures using the following colors: Smc1 = red; Smc3 = blue; Scc1 = green; Scc2/Nipbl = yellow; Scc3/SA1/SA2 = orange; Pds5 = violet; Wapl = cyan; Eco1/Esco1/Esco2 = ivory; Sororin = gray.AF predictions prior to February 2023 were performed via Google CoLab using Alphafold 2, while those performed thereafter used Colabfold v1.5.2 AF2 with MMSeqs2. Supposition of different models were performed using UCSF Chimera while all figures in this article and Video 1 (available at WAPL deletions were introduced into the genome of S. cerevisiae using CRISPR-Cas9. Cells of strain KN18714 (a W303-related strain whose endogenous WAPL gene was tagged with EGFP) were transformed with a 738 nt (for Δ4–9) or a 138 nt (for Δ208–215) PCR ‘healing fragment’ along with a 2 micron LEU2 plasmid, constitutively expressing Cas9 endonuclease and transcribing the structural RNA that contains the Cas9 binding site and the guide RNA that will target the double-strand break formation by Cas9 to the WAPL (RAD61) gene. The guide RNA was designed 20 nt upstream of the NGG  using the annealed oligos MB526F/MB527R  and fused afterward at the 5′ end of the structural RNA with Sap1 cloning. The PCR ‘healing fragment’ was created using the oligos MB543F/MB525R for Δ4–9 and MB534F/MB525R for Δ208–215  and contained the deletion, a mutagenized NGG Cas9 sequence target  and 5′-, 3′- homology arms needed for repair of the double-strand break created by Cas9. As a control, no colonies were obtained after yeast transformation of the 2 micron LEU2 plasmid alone without the ‘healing fragment.’ All mutations were confirmed by sequencing the genomic locus.The WAPL mutations to suppress eco1Δ lethality was tested by crossing them with strain KN16432, which carries deletions of both ECO1 and WAPL. Dissection of 18 tetrads from crosses involving WAPLΔ208–215 yielded 60 viable spores, of which 27 carried eco1Δ. Of these, 15 carried the waplΔ and 12 the WAPLΔ208–215 mutation. Thus, WAPL Δ208–215 was as effective as waplΔ in suppressing eco1Δ lethality and this allele is therefore defective in RA. In contrast, dissection of 18 tetrads from crosses involving WAPLΔ4–9 yielded 49 viable spores of which 16 carried eco1Δ. Of these, all 16 carried waplΔ. Thus, WAPLΔ4–9 cannot suppress eco1Δ lethality and is therefore proficient for RA. Separate experiments demonstrated that WAPLΔ208–215 suppressed eco1Δ when untagged or tagged with PK9 while WAPLΔ4–9 never did so. Mutation of both motifs (Δ4–9 Δ208–215) also suppressed eco1Δ lethality as did an allele in which the TYxxxR[T/S]ΦL motif (YGKKRTIL) was inserted at Δ4–9 (creating the sequence MRAYGKKRTILVLR) within Δ4–9 Δ208–215. In other words, the TYxxxR[T/S]ΦL motif does not appear to function when placed at Wapl’s N-terminus, namely when further from its CTD.The ability of the EGFP-tagged  important study makes use of AlphaFold2 to predict the models of tens of cohesin subcomplexes from different species. The models, which are in most cases consistent with published cohesin variants with compromised in vitro and in vivo cohesin activity, provide convincing evidence that leads to testable hypotheses of cohesin dynamics and regulation. More broadly, this study serves as an example of how to use AlphaFold2 to build models of protein complexes that involve the docking of flexible regions to globular domains.This  There are a number of outstanding questions concerning how cohesin turnover on DNA is controlled by various accessory factors and how such turnover is controlled by post-translational modification. In this paper, Nasmyth et al. perform a series of AlphaFold structure predictions that aim to address several of these outstanding questions. Their structure predictions suggest that the release factor WAPL forms a ternary complex with PDS5 and SA/SCC3. This ternary complex appears to be able to bind the N-terminal end of SCC1, suggesting how formation of such a complex could stabilize an open state of the cohesin ring. Additional calculations suggest how the Eco/ESCO acetyltransferases and Sororin engage the SMC3 head domain presumably to protect against WAPL-mediated release.This work thus demonstrates the power of AF prediction methods and how they can lead to a number of interesting and testable hypotheses that can transform our understanding of cohesin regulation. These findings require orthogonal experimental validation, but authors argue convincingly that such validation should not be a pre-requisite to publication.In their revised version, the authors did not systematically include model confidence scores, and it therefore remains difficult for the reader to evaluate the reliability of the models obtained. The authors correctly point out that such metrics are available on figshare. It is therefore possible to obtain such information. The caveat is that it remains to the user to identify and extract the relevant information. While they claim that they have labeled N- and C-termini in their figures, no such labeling can be seen in the revised version. Addition of such labels, at least for some of the figures, would help the user to navigate the models.The authors have now updated figure legends to indicate which protein is referred to by the chain labels shown in PAE plots.It is exciting to see AF-multimer predictions being applied to cohesin. As some of the reported interactions are not universally conserved and some involve relatively small interfaces the possibility arises that these interfaces show poor or borderline confidence scores. As some of these interfaces map to mutants that have previously been obtained by hypothesis-free genetic screens and mutational analyses, they appear nevertheless valid. Thus, an important point to make is that even interfaces that show modest confidence scores may turn out to be valid while others may be not. The ATPase protein machine cohesin shapes the genome by loop extrusion and holds sister chromatids together by topological entrapment. When executing these functions, cohesin is tightly regulated by multiple cofactors, such as Scc2/Nipbl, Pds5, Wapl, and Eco1/Esco1/2, and it undergoes dynamic conformational changes with ATP binding and hydrolysis. The mechanisms by which cohesin extrudes DNA loops and medicates siter-chromatid cohesion are still not understood. A major reason for the lack of understanding of cohesin dynamics and regulation is the failure to capture the structures of intact cohesin in different nucleotide-bound states and in complex with various regulators. So far only the ATP state cohesin bound to NIPBL and DNA have been experimentally determined.In this manuscript, Nasmyth et al. made use of the powerful protein structure prediction tool, AlphaFold2 (AF), to predict the models of tens of cohesin subcomplexes from different species. The results provide important insight into how the Smc3-Scc1 DNA exiting gate is opened, how Pds5 and Wapl maintain the opened gate, how Pds5 and Scc3/SA recruit different cofactors, how Eco1 and Sororin antagonize Wapl, and how Scc2/Nipbl interacts with Scc3/SA. The models are for the most part consistent with published mutations in these proteins that affect cohesin's functions in vitro and in vivo and raise testable hypotheses of cohesin dynamics and regulation. This study also serves as an example of how to use AF to build models of protein complexes that involve the docking of flexible regions to globular domains. The following is the authors’ response to the previous reviews.We are grateful for the helpful comments of both reviewers and have revised our manuscript with them in mind.https://doi.org/10.6084/m9.figshare.22567318.v2, a fact that the reviewers appear to have overlooked. The Figshare link is mentioned three times in the manuscript. Embedding these data within the manuscript itself would in our view add even more details and we have therefore not included them in our revised manuscript. Likewise, it is rather simple for any reader to work out which part of a PAE matrix corresponds to an interaction observed in the corresponding pdb prediction. Besides which, it is our view that the biological plausibility and explanatory power of models is just as important as AF metrics in judging whether they may be correct, as is indeed also the case for most experimental work.One of the main issues raised was that readers may by default assume that our models are correct. We in fact made it very clear in our discussion that the models are merely hypotheses that will need testing by “wet” experiments and we do not therefore agree that even readers unfamiliar with AF would assume that the models must be correct. It was also suggested that readers could be reassured by including extensive confidence estimates such as PAE plots. As it happens, every single model described in the manuscript had reasonably high PAE scores and more crucially the entire collection of output files, including PAE data, are readily accessible on Figshare at Another important point was that the manuscript was too long and not readable. Yes, it is long and it could well be argued that we could have written a different type of manuscript, focusing entirely on what is possibly the simplest and most important finding, namely that our AF models suggest that in animal cells Wapl appears to form a quarternary complex with SA, Pds5, and Scc1 in a manner suggesting that a key function of Wapl’s conserved CTD is to sequester Scc1’s Nterminal domain after it has dissociated from Smc3. For right or for wrong, we decided that this story could not be presented on its own but also required (1) an explanation for how Scc1 is induced to dissociate from Smc3 in the first place and (2) how to explain that the quarternary complex predicted for animal cells was not initially predicted for fungi such as yeast. The yeast situation was an exception that clearly needed explaining if the theory was to have any generality and it turned out that delving into the intricate details of the genetics of releasing activity in yeast was eventually required and yielded valuable new insights. We also believe that our work on the recruitment of Eco/Esco acetyl transferases to cohesin and the finding that sororin binds to the Smc3/Scc1 interface also provided important insight into how releasing activity is regulated. We acknowledge that the paper is indeed long but do not think that it is badly written. It is above all a long and complex story that in our view reveals numerous novel insights into how cohesin’s association with chromosomes is regulated and have endeavoured to eliminate any excessive speculation. We feel it is not our fault that cohesin uses complex mechanisms.Notwithstanding these considerations, we have in fact simplified a few sections and removed one or two others but acknowledge that we have not made substantial cuts.It was pointed out that a key feature of our modelling, namely the predicted association of Wapl’s C-terminal domain with SA/Scc3’s CES is inconsistent with published biochemical data. The AF predictions for this interface are universally robust in all eukaryotic lineages and crucially fully consistent with published and unimpeachable genetic data. We note that any model that explains all findings is bound to be wrong for the very simple reason that some of these findings will prove to be incorrect. There is therefore an art in Science of judging which data must be explained and accommodated and which should be ignored. In this particular case, we chose to ignore the biochemistry. Time will tell whether our judgement proves correct.Last but not least, it was suggested that we might provide some experimental support for our proposed SA/Scc3-Pds5-Scc1-WaplC quaternary complex. We are in fact working on this by introducing cysteine pairs (that can be crosslinked in cells) into the proposed interfaces but decided that such studies should be the topic of a subsequent publication. It would be impossible with the resources available to our labs to follow up all of the potential interactions and we therefore decided to exclude all such experiments.We are grateful for the detailed comments provided by both reviewers, many of which were very helpful, and in many but not all cases have amended the manuscript accordingly.With regard to the more specific comments:Reviewer #1 (Recommendations For The Authors):1. One concern is that observed interfaces/complexes arise because AF-multimer will aim to pack exposed, conserved and hydrophobic surfaces or regions that contain charge complementarity. The risk is that pairwise interaction screens can result in false positive & non-physiological interactions. It is therefore important to report the level of model confidence obtained for such AF calculations:A) The authors should color the key models according to pLDDT scores obtained as reported by AF. This would allow the reader to judge the estimated accuracy of the backbone and side chain rotamers obtained. At least for the key models and interactions it would be important to know if the pLDDT score is >90 (Correct backbone and most rotamers) or >70 (only backbone is correct).https://pubmed.ncbi.nlm.nih.gov/35679397/ (Supplementary data)B) It would also be important to report the PAE plots to allow estimation of the expected position error for most of the important interactions. pLDDT coloring and PEA plots can be shown side-by-side as shown in other published data (e.g. C) The authors should include a Table showing the confidence of template modeling scores for the predicted protein interfaces as ipTM, ipTM+pTM as reported by AlphaFold-multimer. Ideally, they would also include DockQ scores but this may not be essential. Addition of such scores would help classification into Incorrect, Acceptable or of high quality. For example, line 1073 et seq the authors show a model of a SCC1SA and ESCO1 complex . Are the modeling scores for these interfaces high? It does not help that the authors show cartoons without side chains? Can the authors provide a close-up view of the two interfaces? Are the amino acids are indeed packed in a manner expected for a protein interface? Can we exclude the possibility that the prediction is obtained merely because the sequence segments (e.g. in ESCO1 & ESCO2) are hydrophobic and conserved?https://doi.org/10.6084/m9.figshare.22567318.v2. In our view, the cartoon versions of the models are easier for a reader to navigate. Anyone interested in the molecular details can look at the models directly.We do not agree that including this level of detail to the text/figures of the manuscript would be suitable. All the relevant data for those who may be sceptical about the models are readily available at Importantly, no amount of statistical analysis can completely validate these models. What is required are further experiments, which will be the topic of further work from our and I dare from other laboratories.D) When they predict an interaction between the SA2:SCC1 complex and Sororin's FGF motif, they find that only 1/5 models show an interaction and that the interaction is dissimilar to that seen of CTCF. Again, it would be helpful to know about modeling scores. Can they show a close-up view of the SORORIN FGF binding interface to see if a realistic binding mode is obtained? Can they indicate the relevant region on the PAE plot?Given that AF greatly favours other interactions of Sororin’s FGF motif over its interaction with SA2-Scc1, we do not agree that dwelling on the latter would serve any purpose.1. Line 996: AF predicts with high confidence an interaction between Eco1 & SMC3hd. What are the ipTM (& DockQ if available) scores. Would the interface score High, Medium or Acceptable?https://doi.org/10.6084/m9.figshare.22567318.v2.As mentioned, see 1. Line 1034 et seq: Eco1/ESCO1/ESCO2 interaction with PDS5. Interface scores need to be shown to determine that the models shown are indeed likely to occur. If these interactions have low model confidence, Fig. 36 and discussion around potential relevance to PDS5-Eco1 orientation relative to the SMC3 head remains highly speculative and could be expunged.https://doi.org/10.6084/m9.figshare.22567318.v2. It should be clear that the predictions are very similar in fungi and animals. Crucially, we know that Pds5 is essential for acetylation in vivo, so the models appear plausible from a biological point of view.See 1. Considering the relatively large interface between ECO1 and SMC3, would the author consider the possibility that in addition to acetylating SMC3's ATPase domain, ECO1 remains bound to cohesin-DNA complex, as proposed for ESCO1 by Rahman et al (10.1073/pnas.1505323112)?This is certainly possible but we would not want to indulge in such speculation.1. E.g. Line 875 but also throughout the text: As there is no labeling of the N- and C-termini in the Figures, is frequently unclear what the authors are referring to when they mention that AF models orient chains in a certain manner.https://doi.org/10.6084/m9.figshare.22567318.v2.Good point. This has been amended. However, the positions of N- and C- is all available at 1. Fig19B: PAE plots: authors should indicate which chains correspond to A, B, C. Which segment corresponds to the TYxxxR[T/S]ΦL motif? Can they highlight this section on the PAE plot?Good point and amended in the revised manuscript.Minor comments:1. Line 440: the WAPL YSR motif is not shown in Fig. 14A1. Line 691: Scc3 spelling error.1. Line 931: Sentence ending '... SCC3 (SCC3N).' requires citation.1. Line 1008: Figure reference seems wrong. It should read: Fig. 34A left and right. Fig. 34B does not contain SCC1.Many thanks for spotting these. Hopefully, all corrected.1. Fig. 41 can be removed as it shows the absence of the interaction of Sororin with SMC1:SCC1. Sufficient to mention in the text that Sororin does not appear to interact with SMC1:SCC1.This is possible but we decided to leave this as is.Reviewer #2 (Recommendations For The Authors):Minor points(1) Are there any predicted models in which one of the two dimer interfaces of the hinge is open when the coiled coils are folded back, as seen in the cryo-EM structure of human cohesin-NIPBL complex in the clamped state?No AF runs ever predicted half opened hinges. It is possible that the introduction of mutations in one of the two interfaces might reveal a half-opened state and we ought to try this. However, it would not be appropriate for this manuscript, we believe.(2) Structures of the SA-Scc1 CES bound to [Y/F]xF motifs from Sgo1 and CTCF have been reported, suggesting that a similar motif could interact with SA/Scc3. Surprisingly, AF did not predict an interaction between Scc3/SA and Wapl FGF motifs, which only bind to the Pds5 WEST region. On the other hand, AF predicted interactions of the Sororin FGF motif with both Pds5 WEST and SA CES. Can the authors comment on this Wapl FGF binding specificity? What will happen if a Wapl fragment lacking the CTD is used in the prediction?This seems to be an academic point as the CTD is always present.


Overeating is driven by both the hedonic component (‘liking’) of food, and the motivation (‘wanting’) to eat it. The nucleus accumbens (NAc) is a key brain center implicated in these processes, but how distinct NAc cell populations encode ‘liking’ and ‘wanting’ to shape overconsumption remains unclear. Here, we probed the roles of NAc D1 and D2 cells in these processes using cell-specific recording and optogenetic manipulation in diverse behavioral paradigms that disentangle reward traits of ‘liking’ and ‘wanting’ related to food choice and overeating in healthy mice. Medial NAc shell D2 cells encoded experience-dependent development of ‘liking’, while D1 cells encoded innate ‘liking’ during the first food taste. Optogenetic control confirmed causal links of D1 and D2 cells to these aspects of ‘liking’. In relation to ‘wanting’, D1 and D2 cells encoded and promoted distinct aspects of food approach: D1 cells interpreted food cues while D2 cells also sustained food-visit-length that facilitates consumption. Finally, at the level of food choice, D1, but not D2, cell activity was sufficient to switch food preference, programming subsequent long-lasting overconsumption. By revealing complementary roles of D1 and D2 cells in consumption, these findings assign neural bases to ‘liking’ and ‘wanting’ in a unifying framework of D1 and D2 cell activity. Overweight and obesity, and eating disorders generally, have increased in recent decades , 2. ThusThe ‘incentive salience theory’ of reward has provided a valuable framework for more than a decade for investigating the role of food hedonics in the context of appetitive behavior in humans and animals , 14. ThiDopaminergic neurotransmission in the nucleus accumbens (NAc) plays a key role in behaviors related to natural rewards and drugs of abuse –26. In tTherefore, increased understanding of which cells in the dopaminergic system underly brain substrates of ‘liking’ and ‘wanting’ in healthy animals may open an avenue to comprehending the impact of food rewards on eating behaviors linked to the development and onset of obesity and other eating disorders. It may also provide insight into brain circuits and mechanisms underlying both hedonic and motivational components of reward, including mechanisms common to both natural rewards and drugs of abuse.This paper aims to further understand the differential contribution of medial NAc shell D1 and D2 cells to different components of palatable food consumption, namely ‘liking’ (hedonia/perceiving food as pleasurable) and ‘wanting’ . We assess ‘liking’ and ‘wanting’ from different angles using multiple behavioral paradigms: (1) a free-consumption task to assess hedonic pleasure experienced from eating a palatable food (this focuses on ‘liking’ rather than ‘wanting’). This included lick microstructure analyses to provide a supplementary read-out of the ‘liking’ and ‘wanting’ components of hedonic eating; (2) a motivational effort-based task assessing ‘wanting’; and (3) a hedonic-shifting task to evaluate how medial NAc shell cells drive choice by inducing either ‘liking’ or ‘wanting’. Alongside those behavioral tests, we employed neuronal recording or optogenetic tools, and multivariate analysis. This enabled us to better understand the forces underlying eating behavior that may lead to overconsumption in healthy animals, and may ultimately lead to the onset of obesity or other eating disorders.To probe the neural mechanisms underpinning reward consumption, we used in vivo fiber photometry to record fluorescence of cre-dependent GCaMP6s calcium indicator selectively expressed in D1 cells (in D1 receptor-cre mice) or D2 cells (in adenosine A2a receptor gene (Adora2a)-cre mice)  in the mTo determine how D1 and D2 cells represented solutions with different palatability, mice were first given free access to water from a bottle Fig. . Once thWe found that before licking, both D1 and D2 cells transiently increased their activity, which peaked just after licking onset. In both D1 and D2 cells, the peak was more pronounced in response to milkshake than water. This may reflect hedonic processes Fig.  rather tMilkshake consumption within a session  was significantly higher than water, and this was consistent across sessions Fig. . This inWe found that licking parameters reflected a greater ‘liking’ for milkshake over water; lick duration, bout duration and bout size increased significantly, while inter-lick interval (ILI) decreased significantly, between the water and first milkshake sessions Figs.  and S2c,Bout number increased, and inter-bout interval (IBI) decreased significantly between water and the first milkshake session, suggesting that mice were more motivated (‘wanted’) to consume milkshake Fig. . IBI als2 change values , explaining around 30% of the variability in bout duration . Specifically, the initial activation in D1 cells at licking onset and the sustained inhibition of D2 cell activity during eating were good predictors of ‘liking’ parameters. It is important to note, however, that prediction does not imply causation, which we next addressed.ad libitum palatable food consumption. Based on previous findings [We next employed optogenetics to investigate how the roles of medial NAc shell D1 and D2 cells differ during findings , 54, we Next, we sought to test whether silencing D1 and D2 cells using the inhibitory opsin eNpHR3.0 could also influence consumption of a palatable food. We hypothesized that D1 cell silencing would induce a decrease in consumption, while D2 cell silencing would prolong consumption. However, silencing D1 cells did not lead to a significant decrease in overall consumption Fig. , but indInhibition of D2 cells did not lead to any significant difference in lick or bout number compared to controls Fig. , suggestad libitum consumption, only D2 cell activation leads to an overall increase in eating, and this is mediated by an increase in ‘wanting’, despite a concomitant decrease in ‘liking’. However, a limitation of the ad libitum paradigm is that it only examines baseline motivation and is not designed to evaluate motivation to work for a reward. Therefore, we next probed the role of D1 and D2 cells in a different motivational task, designed to interrogate ‘wanting’.Taken together, these results show that during To probe ‘wanting’ further, we paired a food reward with a cue, which allowed us to investigate the attribution of ‘incentive salience’ to the cue. We exposed mice to a Pavlovian ‘motivation to retrieve a reward’ task in which they received food rewards announced by a sound cue, which they had to retrieve from a food magazine. This did not require the mice to develop learning strategies. This allowed us to monitor three parameters: the number of entries in the food magazine, the time to retrieve and consume the food after cue onset (latency), and the length of food-magazine entries Fig. .Fig. 3NAad libitum paradigm where D1 cell signal increased prior to licking onset, here, in a motivational task, the increase in signal was specifically aligned to the reward delivery cue. During food magazine visits , the neural dynamics observed were similar to the first ad libitum study, with a sustained decrease in the activity of D2 cells  of the pre-food magazine visit D2 cell signal , with optogenetic stimulation  of either D1 or D2 cells could shift natural preference for one flavor to another. We hypothesized that optogenetic stimulation of either population would establish a learned preference for the least innately preferred flavor by increasing the hedonic value (‘liking’) of the CS (flavored food).To assess innate preference, mice were first presented with two bottles, each containing a different flavor, but with identical nutritional composition (‘pre-test’). The least preferred flavor was then coupled to optogenetic stimulation of either D1 or D2 cells during a daily conditioning session over several days, while the preferred flavor was presented unpaired with optogenetic stimulation in a separate daily session Fig. . FollowiDuring optogenetic activation of D2 cells, consumption of the paired and unpaired flavors across the conditioning phase did not differ Fig. . Note thIn addition, we used lick microstructure analysis to assess potential changes in ‘liking’ and ‘wanting’ produced by flavor preference conditioning. During the conditioning sessions, stimulation of D1, but not D2 cells increased ‘liking’ (increased lick duration and decreased inter-lick duration) over time Fig. , left. TThese results show an overall preference for the flavor coupled with D1 cell stimulation, thus demonstrating an important role for D1 cells in promoting the consumption of preferred palatable foods.Our study reveals distinct patterns of NAc D1 and D2 cell activity during different behavioral paradigms intended to assess different psychological processes of hedonic behaviors: hedonia (‘liking’ a food) and motivation (‘wanting’ the food). In a free palatable food consumption paradigm, the magnitude of natural D2 cell inhibition following consumption onset was found to be a good predictor for ‘liking’. In contrast, D1 cell activity promotes ‘liking’ during free consumption. In the operant task, we showed that D1 cell activity after cue and right after eating onset drives the motivation to retrieve palatable food, while reduced activity of D2 cells after food magazine visits drives the motivation to remain in the magazine. Lastly, we show that D1 cell stimulation induced hedonic shifting in a Pavlovian task, leading to overconsumption of palatable food. This effect was specific to D1 cells, as stimulation of D2 cells during opto-paired food conditioning had no effect across conditioning days or during the choice test . These effects on motivation, although inconsistent with some previous findings which suggested that D2 cells are associated with negative rather than positive valence events , 28, 54,In our regression analyses, we included the standard deviation in the signals before licking/food magazine visit (‘std pre’) as a way to explore how neuronal activity variability relates to behavioral variability . In the In the hedonic-shifting choice test after the final optogenetic stimulation pairing, D1 but not D2 cell activity significantly increased preference for the flavor that was previously least preferred. This indicates that D1 cell optostimulation induced a hedonic preference via an association between the flavor and the rewarding effects of stimulation Fig. . This waIn this experiment we were able to induce a learned shift in preference for a particular flavor when D1 cell optogenetic stimulation was coupled with a less preferred flavor. This is based on a Pavlovian association formed between NAc D1 cell activation (US) and the least preferred flavor (CS), which changed the taste preference to that flavor. This suggests that hedonic ‘liking’ of a flavor stimulus can be determined not only by flavor itself, but also by the relevant ‘brain’ state at the time of formation of Pavlovian associations, for example by a change in perceived palatability. In a similar manner, in the context of drug addiction, it was shown that NAc D1 cell activity preceding entry to the drug-paired compartment in a conditioned place preference test is needed for drug-associative learning to take place , 73.That our findings are not always in line with previous literature may also reflect the discordance between previous studies themselves, based on the precise NAc subregion targeted, which plays a role in the results obtained , 92, thePrevious studies have shown that NAc shell dopamine release during reward delivery, but not during the preceding cue, increases preference for the dopamine-release-associated reward . This woIt was also shown that in the early stage of an associative learning task, NAc dopamine release occurred at reward delivery and not cue onset, but that at later stages of the task  the release occurs at cue onset but not reward , 98. In Our findings have potential implications for human eating disorders. ‘Hedonic eating’ does not necessarily involve pleasure, but can also be driven by a motivation to eat which can be dissociated from the drive caused by a nutrient deficit , 102. ItTo our knowledge, our study is the first to approach aspects of NAc dependent reward consumption using such a multi-faceted approach. We incorporated observation, inhibition and excitation of NAc D1 and D2 cells, multiple behavioral paradigms investigating different aspects of ‘liking’ and ‘wanting’, including analysis of lick microstructure, and multivariate analysis. In summary, we show that NAc D1 cell activity plays a role in reinforced behaviors induced by palatable foods in an operant paradigm or a Pavlovian association test. However, in a palatable food free-access paradigm D1 cells encode only the hedonic property of the palatable food. NAc D2 cells on the other hand, have a role in the drive to retrieve food, showing elevated activity prior to eating onset in a motivational task. Once eating starts, suppressed D2 cell activity allows for prolonged eating episodes. This would support the idea that evolutionary processes led to these two cell populations that can independently and mutually influence hedonic eating. D1 cells are prone to lead to a maladaptive cycle of increased ‘liking’ and ‘wanting’, thus driving overeating, while D2 cells cannot reinforce both processes and are faced with a tradeoff between increasing ‘wanting’ by being more active or allowing ‘liking’ by remaining silent Figs.  and S10.ad libitum. In order to avoid food deprivation for behavioral testing, mice were provided with 2% citric acid in water ad libitum in their home cage [All animal procedures were carried out in accordance with the Animal Welfare Ordinance (TSchV 455.1) of the Swiss Federal Food Safety and Veterinary Office and were approved by the Zurich Cantonal Veterinary Office. Mice were kept on a reversed 12-h/12-h light/dark cycle and provided with standard chow pellets ome cage . As the For specific targeting of D1 cells and D2 cells, D1-cre (B6.FVB(Cg)-Tg(Drd1-cre)EY262Gsat/Mmucd) and Adora2a-cre (Adora2aTg(Adora2a-cre)KG139Gsat, originally obtained from the Jackson Laboratory) mouse lines were used, respectively. For photometry recordings, 6 mice from each line were injected bilaterally with a pAAV9-CAG-Flex.GCaMP6s.WPRE.SV40 virus  in the medial NAc shell (D1-cre and D2(A2a)-cre mice; bregma +1.4 mm, midline ± 1.35 mm, depth 3.9 mm, at an angle of 10°) and were then implanted bilaterally with optic fibers , using the same coordinates (except that the depth of the fiber tip was reduced to 3.8 mm).13 vg/mL) or ssAAV-9/2-hEF1a-dlox-eNpHR3.0_iRFP(rev)-dlox-WPRE-hGHp(A)  or pAAV-flex-GFP  for activation, inhibition and control respectively. Viruses and fiber implants were positioned at the same coordinates as the photometry cohort. Virus expression was assessed using standard histology methods.For optogenetic modulation, mice (see Table For stereotaxic surgeries (Kopf Instruments), a small craniotomy of 0.5 mm in diameter was performed above the injection sites using a microdrill. Injections were performed using a Nanoject III injector and glass capillaries (Süd-Laborbedarf Gaunting); 120 nL of virus were injected on each side at a rate of 1 nL/sec before optic fibers were slowly inserted and fixed to the skull using Super-Bond . Mice were allowed 6 weeks to recover before starting experiments.Photometry recordings were performed using a camera-based bundle-imaging fiber photometry system (Doric Lenses) using interleaved illumination produced by two LEDs . Fluorescence produced by 405-nm excitation provided a real-time control for motion artifacts. Recordings were performed using the Doric Neuroscience Studio software.Optogenetic activation of D1-ChrimsonR and D2-ChrimsonR cells was performed using a red laser . Laser output frequency was 4-ms pulses at 20 Hz, driven by an Arduino board. Optogenetic inhibition of D1-eNpHR3.0 and D2-eNpHR3.0 cells was achieved with a yellow laser . The laser delivered a constant light when inhibition was triggered and ramped down at the end of an inhibition bout to avoid neuronal activity rebound. D1-GFP and D2-GFP control mice were split evenly between opto-inhibitory and opto-excitatory stimulation patterns to exclude heating and ipsiversive effects of either pattern . All micThe lasers were connected to the bilateral fiber implants using a 1 × 2 fiber-optic rotary joint (Doric Lenses) and yielded an output power of ∼8 mW at the end of each fiber tip. Laser pulse onset parameters are described in each relevant experiment section.Virus location and location of fibers were checked in all mice from both the Photometry and Optogenetics cohorts using standard histology methods.Prior to surgeries, both photometry and optogenetic cohorts mice were screened in this simple reward delivery task. Mice were habituated to the experimental chambers  for 20 min per day for two days. The task schedule was designed and controlled by ABET II software (Lafayette Instrument). On three consecutive days, mice could receive up to 15 milkshake deliveries  during a maximum of 30 min. The session ended when whichever of the 15 deliveries or 30 min came first. The inter-trial interval varied pseudo-randomly, 30 ± 10 sec, counted from the moment a mouse exited the food magazine. Only those able to perform the task in 10 min or less were implanted. This was an indication that mice made the association between the cue and food delivery. The house light (white light) was on during the sessions. The tray light was turned on at each milkshake delivery and turned off when the mouse visited the food magazine. Milkshake delivery was associated with a sound cue. Consumption onset was defined as the first food magazine IR beam break following each delivery.Photometry Recordings section for details).The same paradigm used for pre-surgery screening was run again on 3 consecutive days while photometry signals were being recorded  consisting of an Optogenetic Controller (Plexon Inc.) and orange LEDs for activation  or lime LEDs for inhibition . The output at the end of each patchcord  was ∼5 mW. The patterns were designed in and controlled by Radiant software (Plexon Inc.) which was itself triggered by ABET II software during the task.Here, the task structure was similar to the pre-surgery screening task, except mice performed the task over two days only. The first day was identical to the pre-screening task and served as a baseline. On the second day, the task was identical but 10 s of optogenetic stimulation either started from milkshake delivery cue  or started at food magazine visit onset . After a few-weeks wash-out period this 2-day experiment was repeated, only this time mice which had received optogenetic stimulation during milkshake delivery now received it during the food magazine visit and vice versa. In these tasks the activation and inhibition patterns were the same as those described in the 2 arenas, Habitest modular mouse test cage, Coulbourn Instruments, USA) and patchcords  was performed on 2 consecutive days before the experiment started. The test cages were placed in closed, sound-attenuating chambers  and illuminated using infra-red light only. Licks were detected using a capacitive sensor  and recorded at 500 Hz throughout the sessions using a National Instruments board  and custom built LabView software . Weights consumed were recorded at the end of each session. Mice were not food deprived for these experiments.Mice were first habituated to spring-based tip bottles (EBECO) in the home cage using water. Habituation to the arenas  were included .On experimental days, mice were placed in the arenas for 20-min long sessions (one session/day for each mouse) with free access to one spring-based tip bottle (EBECO) containing water (days 1–3), strawberry milkshake (days 4–8). Concomitant photometry signals were recorded as described in the Optogenetics section) every time a mouse started licking and continued for the duration of licking.Over a 20-min session with access to milkshake, laser stimulation started (according to the patterns described in the For this experiment only ChrimsonR mice (and their respective GFP controls) were used. Mice were habituated in the home-cage to two novel flavors of milkshake which were matched for their calorie and macro-nutrient contents: a chocolate-flavored and a vanilla-flavored milkshake .Ad libitum consumption experiment section. Two spring-based tip bottles (EBECO), each with a different flavor of milkshake were freely available. The positions of the bottles in the cage were counterbalanced across mice. Licking and amounts consumed were recorded as described in the Ad libitum consumption experiment section. The time spent licking at each bottle on the second day of the pre-test phase was used to determine which was each mouse’s preferred flavour.This phase served to test the innate preference of mice for one flavor over the other. Testing was performed over two consecutive days with 20-min sessions in the same Coulbourn Instruments chambers as described in the Optogenetics and Ad libitum consumption experiment sections respectively.Two 10-min conditioning sessions were run, one in the morning, and one in the afternoon for 7 consecutive days. In each of these sessions only one flavor was available at a time, and the order of the sessions was swapped each day see Fig. . Licking24 h after conditioning, mice were assessed for their preference again. During the choice-test, both flavors were presented simultaneously for 20 min with no optogenetic stimulation on either flavor.Throughout the conditioning and choice-test phases, but not for the initial preference pre-test phase, mice were mildly food-deprived.Single lick durations, defined as an uninterrupted high signal.Inter-lick intervals (ILI), defined as an uninterrupted low in the signal of up to 250 ms. An upper boundary was set in accordance to Johnson , to distNumber of licking bouts, a bout was defined as a licking episode of at least 500 ms, allowing for interruptions within a bout of up to 250 ms.Licking bout duration, length of a given bout in seconds.Licking bout size, number of individual licks within a given bout.Inter-bout intervals (IBI), defined as interruptions in licking of more than 250 ms allowing for licking occurrences of up to 250 ms, i.e., a short lick was not considered a bout and thus does not interrupt a given IBI.Based on previous literature , 45, licFor the regression analysis (see below), licking bouts had a slightly more inclusive definition, starting with at least 100 ms of licking and allowing for pauses of up to 1 sec.Correlations between photometry signals and ‘liking’/‘wanting’ parameters were performed using the SPSS (IBM Corp. Version 28) linear regression tool and following the regression method described in Field . A subseWhen analyzing photometry signals around food delivery, trials where the mouse entered the food magazine within 5 sec after food delivery cue were not included so that signals did not include food consumption, but only food anticipation Fig. . For anap value ≤ 0.05 was considered to indicate significance. In the motivation task . All statistical tests and descriptive statistics were performed using SPSS software (IBM Corp. Version 28). Sex was taken as a factor in the analysis but did not reveal significant differences in all comparisons. Therefore, since sex was not the primary independent variable of interest in this study, and given the lack of statistical differences, we pooled data from both sexes for all further analyses. Tests and their results are presented in the figure legends. Unless otherwise stated, data are presented as mean ± SEM, and a ask Fig. , outlierSupplementary Figures


Biomechanical cues from the extracellular matrix (ECM) are essential for directing many cellular processes, from normal development and repair, to disease progression. To better understand cell-matrix interactions, we have developed a new instrument named ‘OptoRheo’ that combines light sheet fluorescence microscopy with particle tracking microrheology. OptoRheo lets us image cells in 3D as they proliferate over several days while simultaneously sensing the mechanical properties of the surrounding extracellular and pericellular matrix at a sub-cellular length scale. OptoRheo can be used in two operational modalities  to extend the dynamic range of microrheology measurements. We corroborated this by characterising the ECM surrounding live breast cancer cells in two distinct culture systems, cell clusters in 3D hydrogels and spheroids in suspension culture. This cutting-edge instrument will transform the exploration of drug transport through complex cell culture matrices and optimise the design of the next-generation of disease models. A new instrument named OptoRheo combines light sheet fluorescence microscopy and particle tracking microrheology for live imaging and micromechanical sensing of extracellular matrix-cell interactions. Simultaneously, the ECM is continuously remodelled by cells as they exert these traction forces5 during cell migration and morphological re-arrangement6. Anomalies in the mechanical properties of the ECM play consequential roles in the development of pathologies such as cancer7 and fibrosis8, often establishing barriers to therapeutic intervention9. Modelling and understanding cellular influence on ECM biomechanics is challenging given the wide range of mechanical environments experienced in health and disease. In healthy tissues, the elastic modulus has been reported to range from tens of Pa  to well above 10 KPa , with disease states such as cancer and fibrosis showing a notable change in stiffness 10. Moreover, the full mechanical characterisation of the ECM also contains a viscous component that may influence cell behaviour11. The recent development of engineered hydrogels with tuneable mechanical properties13 have made it possible to recreate elements of the ECM micro-architecture in vitro and reveal the influence of ECM viscoelasticity on cell processes15. Despite these advances and their importance, the mechanistic processes of cell-matrix interactions remain poorly understood. For instance, do cells ‘prime’ their local environment prior to migrating or do they exploit existing weaknesses in the ECM and migrate accordingly? These unanswered questions call for minimally invasive optical approaches to monitor changes in the microscopic mechanical properties of the ECM, in situ and in real time, local to proliferating cells over many days.Cells sense and respond to the mechanical properties of the extracellular matrix (ECM) at a cellular length scale, using traction forces to probe stiffness19 built on a commercial inverted microscope body to image hundreds of microns deep from the coverslip, within live 3D cell cultures and with sub-cellular resolution. The sample is kept completely stationary during z-scanning, with no perturbation or contamination risk from dipping lenses, both crucial for ensuring that observation does not influence the mechanical or biological properties of the sample. This configuration allows for delicate samples such as hydrogel scaffolds to be imaged simply in off-the-shelf chambered coverslips. To extract the viscoelastic properties of the ECM, OptoRheo tracks the thermally driven Brownian motion of micron-sized beads, acting as rheological probes across a wide time-window. The inert bead probes can be embedded in the hydrogel during encapsulation22 or even internalised into cells23 to probe intra-cellular viscoelasticity. In suspension cultures, an optical trap can be used to hold the probe in the field of view within the cells’ microniche during the measurement extending the range of materials the instrument can characterise24. Finally, OptoRheo incorporates optional multiplane imaging that can be used to extend microrheology to 3D in a configuration similar to the one developed for OptiMuM25, to achieve a full 3D characterisation of the extracellular microenvironment.To address this aim, OptoRheo combines three different microscopy techniques, light sheet microscopy, multiplane microscopy and optical trapping into a single instrument. This approach enables live fluorescence imaging deep in 3D cell cultures and microrheology measurements of the ECM within the same region of interest, local to and far from the cells. 3D fluorescence imaging is achieved using a new version of reflected light sheet fluorescence microscopy (LSFM)To highlight the capability of OptoRheo, we present data obtained from the analysis of two systems seeded with human-derived MCF-7 and/or MDA-MB-231 breast cancer cells, either (I) encapsulated as clusters in 3D hydrogels or (II) as spheroids maintained in suspension culture. In the case of hydrogel scaffolds, matrix stiffness was measured using passive particle tracking microrheology without the use of an optical trap or multiplane imaging, whereas in the case of suspension cultures, the optical trap was implemented along with multiplane imaging. Imaging and microrheology were performed sequentially at multiple regions within the samples at depths of 150 µm - 400 µm from the coverslip. In the case of the hydrogels, the samples were monitored over three days to reveal microscale variations in the elastic properties of the ECM near to and away from cells. When studying spheroids in suspension the optical trap was used to place and hold the probes in user-defined locations and extract the relative viscosity of the media near the spheroids. Notably, in both the cases our measurements were found to be sensitive to local spatio-temporal variations in the biomechanical properties of the culture medium. As demonstrated in this study, our multimodal and minimally invasive approach opens a wide range of future opportunities for physiologically relevant, long-time course investigations and time-lapse videos of cell-ECM interactions in fragile live cell culture samples. We anticipate that this approach will be applied to increasingly complex and relevant in vitro models, providing an essential insight into the previously opaque mechanistic control of cell behaviour by the ECM in health and disease.27. For deep imaging of live 3D cell cultures, the light-sheet illumination was introduced using a 10 mm 90:10 (Reflectance: Transmittance) beam splitter cube placed in the sample chamber prior to casting the gel alongside it   making them unsuitable for multicolour imaging. OptoRheo is fitted with a stage-top incubator that regulates temperature, CO2 and humidity around the sample, allowing long time-course experiments spanning over hours  of 1.1, selected to image deep into a sample, but with a high enough NA for optical trapping. For the data presented in this work, z-scans were typically recorded 150–400 µm from the coverslip Fig. . To exteurs Fig.  and days31. This involves a statistical analysis of the residual Brownian motion of micron-sized spherical probes, whose temporal behaviour can be described by means of the Generalised Langevin equation31. For this purpose, polystyrene microsphere probes were seeded in the cell culture samples under sterile conditions  tracking of the probe trajectories, which are needed for broadband microrheology calculations. Therefore, when working with liquids we used an optical trap  by controlling the laser power, to maximise the amplitude of the residual Brownian motion, increasing the sensitivity of the microrheology measurement and the frequency range. The confined Brownian motion of the microsphere could then be recorded at ~300 Hz in 3D by inserting a removable pair of quadratic gratings (QG) in the detection path before the sCMOS camera to achieve multiplane detection of the particle position33  over this large range of distances from the coverslip. Relative viscosity (ratio of viscosity of an aqueous solution to the viscosity of water at the same temperature) could be evaluated in 3D by analysing the normalised position autocorrelation function (NPAF) for x, y and z at depths ranging from 50 µm to 400 µm from the coverslip. In particular, the relative viscosity can be read “at a glance” from the abscissa of the NPAF intercept 35  from the same locations to be captured in separate colour channels. These volume images could be overlaid and combined with microrheology measurements to map the changing biomechanical properties to the changing morphology of the sample, at the microscale. This approach was used to compare four different complex systems made of hydrogels with and without (i) collagen and (ii) cells.In order to test the ability of OptoRheo to evaluate biomechanical properties of the ECM in real time, clusters of human-derived MCF-7 cancer cells expressing the tdTomato fluorescent protein were grown in a hydrogel-encapsulated cell culture matrix39 with a rubbery elastic plateau The curves of the elastic  as compared to gels supplemented with collagen I (n = 11) . There was a significant difference in the proportional change in p = 0.007, Kruskal-Wallis test; Fig. The presence of cells brings about complex changes in the gels which become more apparent over time and when taking into account the proximity to cell clusters Fig. b–f. Overμm) Fig.  hinting The second scenario tested as a proof-of-concept, was to acquire 3D images and microrheology measurements near spheroids in suspension culture. Spheroids were grown from the same MCF-7 cancer cell line as in the peptide hydrogel cultures and were used two days after seeding at a size of ~1 mm in diameter. As both 3D imaging and microrheology on OptoRheo do not involve moving the sample, these spheroids could be maintained in liquid media without the need to immobilise them in agarose or any other hydrogel matrix—an advantage over most conventional LSFM instruments. Volume images near the edges of spheroids were acquired by tiling multiple overlapping imaging volumes of 200 µm × 200 µm × 200 µm (between 150 and 350 µm from the coverslip) Fig. . Once thn = 4; 6 µm, n = 4; 12 µm, n = 3 and 30 µm, n = 7) between the centre of the microsphere and the surface of the spheroid (N = 2 spheroids)  Fig. . This tr2 throughout experiments, enabling delicate hydrogel-based cell culture samples to be studied over multiple days. With the ability to optically trap microrheological probes when required, we have demonstrated the capability of the instrument to study aqueous (suspension culture) as well as soft solid (hydrogel) environments. This modular functionality with a gratings-based approach allows OptoRheo to transition from 2D to 3D particle tracking without difficulty. We provide experimental evidence of this approach by following changes in matrix viscoelasticity in 2D in hydrogel-encapsulated cell cultures over three days and viscosity in 3D near spheroids in suspension. Our localised rheological measurements reveal heterogeneities at the microscale in hydrogel-encapsulated cell cultures.To understand how cells interact with and remodel their surrounding matrix, it is crucial not only to visualise cell clusters in 3D, but also to map these images to the changing micro-mechanical properties of the matrix local to and distant from the cells. In this study, we have introduced an integrated instrument—OptoRheo, that combines light sheet fluorescence microscopy (LSFM) with non-invasive microrheology to enable a more complete understanding of cell-matrix interactions. The new LSFM configuration presented here is straightforward to implement and does not require the use of bespoke, expensive optics. This reflected configuration allows for samples to be prepared and mounted as on any commercial inverted microscope, using off-the-self sample chambers and a stage-top incubator to control temperature, humidity and CO44, but which is limited to a narrow range of frequencies in the gigahertz regime. It is possible that the narrow frequency range of Brillouin scattering measurements will miss some of the mechanical changes we report here. Furthermore, compared to previously published methods for optical trapping based stiffness measurements46, OptoRheo can characterise samples at depths of hundreds of microns from the coverslip making it particularly suited to cell cultures grown in 3D. OptoRheo has the additional benefit that the mechanical characterisation is paired with multi-channel 3D fluorescence light-sheet imaging allowing the changing morphology of the cells to be monitored at the same time.OptoRheo provides broadband microrheology measurements, covering 5 - 6 decades of frequency in our data. These broadband measurements provide valuable insight into the frequency-dependent mechanical properties of biological materials. As can be seen in Fig. 25. Efforts to extend these 3D analyses to gels in the future would be highly valuable as biophysical properties in the ECM are likely to vary in 3D, as apparent in the images of labelled collagen present in the gel samples  can be related to viscous and elastic components of the matrix biophysical properties. In future, it would be relatively straight forward to include additional probes into the sample  to track the coordinated impact of chemical, biological and mechanical cues.47, the control of cell behaviour by mechanical forces exerted through the ECM remains poorly understood, even as researchers take advantage of ECM control to create more complex, physiologically relevant models of development and disease, that better represent the in vivo micro-environment. The ability to integrate read-outs of cell behaviour with the microrheology of pericellular and distant matrix will be critical in further improving these models and using them to uncover the mechanistic basis of the phenomena they imitate. In addition, for development of therapeutics, there remains a conspicuous gap in our understanding of the environments that drugs and delivery vehicles encounter in the body. The simultaneous observation of material transport together with rheological measurements will enable us to build detailed structure-function relations of drug delivery pathways, which in turn, will enable more efficient screening of candidate therapeutics and better predictive models of in vivo activity and efficacy.As highlighted in recent publications2 gas.OptoRheo uses a reflected light-sheet configuration where light-sheet illumination is introduced into the imaging plane of an inverted microscope (Olympus IX-73) using a right-angle optic . Z-scanning was achieved by moving the light sheet through the sample using a galvanometer scanning mirror  whilst simultaneously moving the objective lens  via a motorised piezo objective scanner  to keep the light sheet in focus. Lateral positioning was achieved using a XY microscope stage  and a zoom-mount attached to the cylindrical lens forming the light sheet. The fluorescence image was detected using an sCMOS camera (Hamamatsu ORCA Flash 4.0 V2). Environmental control was achieved using an Okolab stage-top incubator (H301-K-FRAME) supplied with pre-mixed COex 473 nm, 2.4 µm for λex 532 nm and 3.3 µm for λex 640 nm. The light sheet was aligned and characterised by imaging it in reflection using two beam splitters  in tandem. The relationship between voltage applied to the galvanometer mirror and position of the light sheet was characterised using this double beam splitter set up and keeping the detection objective stationary while scanning the light sheet in z. The slope of the linear fit to the measured position of the light sheet against the voltage applied gave the pixel to voltage step size for synchronised movement.Multi-colour fluorescence imaging was made possible by using three lasers separately to form the light sheet; 473 nm , 532 nm  and 640 nm . These laser lines were coupled to each other in the illumination beam path using dichroic mirrors. A cylindrical lens  was used to generate the light sheet with a beam waist of 2.6 µm for λWhen imaging samples, an autofocus step is first performed to ensure the illumination and detection optics, primarily the objective, are aligned. This involves recording a stack of images while keeping the imaging objective stationary and scanning the light sheet with a sub-beam-waist step size. The frame with the highest mean intensity value denotes where the light sheet waist coincides with the imaging plane and so the position of the light sheet for this frame is synchronised with the height of the objective.Standard off-the-shelf beam splitter cubes have a blunt edge that make the bottom 150 µm unsuitable for reflecting the light sheet illumination. These regions can be illuminated by tilting the light sheet at BS1 . Image volumes were saved as ‘.tiff’ files. Automation of the laser lines through camera-controlled remote triggering and a motorised filter turret enabled overnight time lapse imaging.48 and volume tile stitching was performed using the BigStitcher49 plugin for Fiji. 3D renderings for Figs. 50.Contrast adjustment and background subtraction was performed on image volumes in ImageJ/ Fiji51 code in MATLAB which computes the shortest distance between a given point and the outer edge of a triangular mesh.To calculate the distance between microspheres and the nearest cell clusters, the centre positions of the microspheres in image coordinate space were extracted from the LSFM images. Although the microspheres  were not fluorescently labelled, they are identifiable in the 3D LSFM images due to light scattering. Mesh renderings of the corresponding cell clusters were exported from FluoRender and these meshes along with positions of the microspheres from the same image volume were used as inputs in the point2trimesh.mFigure 0′) Fig. c, d or v0′) Fig.  inset atThe beam path from a continuous wave 1064 nm 5 W DPSS laser  was directed into the inverted microscope body and focused in the image plane using the same objective lens used for imaging in the LSFM set up. This objective lens was also used to image a small region of interest (14 μm × 14 μm), required for high frame rate imaging, around the trapped polystyrene microspheres in wide-field with illumination in transmission for fast (300 Hz for multiplane 3D rheology) tracking of thermal fluctuations.25 and its predecessors52. Here, a multiplane grating pair was formed using two quadratically distorted diffraction gratings etched into a quartz substrate (bespoke production by Photronics UK Ltd). A single grating generates three sub-images, corresponding to the m = 0, ±1 diffraction orders while two gratings with orthogonal etch patterns, can generate nine different sub-images, each corresponding to a different image depth which can be captured simultaneously on a single camera sensor (Hamamatsu ORCA Flash 4.0 V2)  Fig. . A 4f im5 particles/ mL. In suspension cultures, the microspheres were added to a final dilution of 1:200,000 from concentrate product, the probes were individually optically trapped using ~4 mW of laser power (at the sample) and moved to a position of interest. The Brownian motion of the microspheres, was recorded over 300,000 frames at ~300 frames per second for experiments in Figs. 6 frames at 5 kHz for Fig. 53 for data in Figs. Particle tracking microrheology was performed using polystyrene microspheres as probes . In hydrogel cultures, the microspheres were encapsulated during the gelation process at a final density of 3 ×1025. Particle tracking with these methods gives us a minimum sensitivity of ~15 nm (FWHM) in the xy plane and ~30 nm (FWHM) in z for a particle with diameter of ~6 μm, using a 60× objective and a plane spacing of ~Δz = 0.79 μm25. A calibration step is performed for each microsphere before taking a measurement by translating a lens . For 2D trajectories along the image plane a centre-of-mass detection method following Otsu’s method of multiple thresholding (with two levels) was used. Out-of-plane Z motion of the probe was tracked by computing a ‘Sharpness’ metric as detailed in our OpTIMuM publication−10 m. These noisy peaks, attributed to instrument noise from the laboratory, were then removed from the data using a custom multiband filter in MATLAB. The MSD values for each filtered trajectory was then fit with a stretched bi-exponential of the formIn the case of hydrogels, the Brownian motion of the microsphere confined within the gel was recorded in 2D without the use of the optical trap or multiplane imaging. For these data, an analysis of the mean squared displacement (MSD) gave the storage (elastic) and loss (viscous) moduli of the gel. To acquire the viscoelastic measurements for each probe, first each experimentally acquired trajectory was detrended to remove long-term drift and a filtering step was performed to remove instrument noise. For the noise filtering, a Fourier transform of each trajectory was used to identify sharp noise peaks characterised by a single frequency width and using an amplitude threshold of 2 × 101, A2, B1, B2, τ1 and τ2 are fitting parameters. This approach mitigates any error generated by the inherently finite nature of the measurements that affects the accuracy to which the MSD is calculated especially at short-time scales  for evaluating Eq. The elastic plateau of the gels 35. This method is highly effective for data aligned with the principal axes of the optical trap  or phenol red free RPMI  with 10% foetal bovine serum , 1% L-glutamine . To maintain the tdTomato protein expression, the medium was supplemented with Puromycin  at 1:1000 every passage (MCF-7 tdTomato) or every 3 weeks at 1:500 (MCF-7 eGFP) or 1:250 (MDA-MB-231 tdTomato). Cells were maintained at 37 °C and 5% CO57. A commercially available peptide preparation in powder form was used as the source of the octapeptide gelator . To form the precursor, a mass of 10 mg peptide preparation was dissolved in 800 μL sterile water , using a 3 min vortex step followed by centrifugation (3 min at 1000 rpm) and a 2 h incubation at 80 °C. After incubation, 0.5 M NaOH  was added incrementally to the gel until optically clear. The gel was vortexed, buffered by addition of 100 μL 10× PBS , and incubated at 80 °C overnight. The resulting precursor could be stored at 4 °C until required.The precursor and gel preparation method was followed as previously published5 particles/mL. The medium/cell suspension was thoroughly mixed with the precursor and collagen-I by gentle (reverse) pipetting, before plating at 100 μL per well into a 4 μ-well glass bottom chambered coverslips  pre-mounted with a beam splitter cube . The wells were then flooded with cell culture medium and incubated at 37 °C and 5% CO2 in a humidified atmosphere. Sequential media changes (at least two) over the next 2 h ensured complete neutralisation and therefore gelation.Prior to peptide gel formation, the precursor was heated at 80 °C until liquid to ensure homogeneity, before transferring to a 37 °C water bath. Cy5 labelled  rat tail Collagen I was neutralised directly before use with 1 M NaOH according to manufacturer instructions, and diluted with sterile water and 10× PBS to a concentration of 1 mg/mL while keeping on ice at all times to prevent polymerisation. Peptide gel formation was then induced by pH neutralisation on addition of cell culture medium (with or without cell suspension) to the gel precursor. A final volume of 1.25 mL was obtained from a preparation by adding 125 μL of cell suspension and 125 μL Cy5 collagen I to a precursor volume of 1 mL. The end concentration of peptide preparation was 8 mg/mL and collagen I concentration was 100 μg/mL. Polystyrene microspheres  were added at final density of approx. 3 × 105 cells were re-suspended in 125 µL cell culture medium, giving final seeding density in the peptide gel 1 × 105 cells/mL. For data in Fig. 6 cells/mL for each cell type. 24 h post encapsulation culture medium was replenished, with the addition of HEPES buffer  at 10 mM final concentration and 0.5–1% penicillin/ streptomycin .For cell encapsulation, the 125 μL volume of cell culture medium was prepared as a cell suspension at 10× the intended final seeding density, to allow for the dilution factor on mixing with the gel precursor. Trypsin-EDTA  was used to detach cells from 2D culture at sub-confluence. 1.25 × 10Prior to casting the gel, the beam splitter cubes were sterilised in absolute ethanol. Cubes were soaked for 1 h, then left to dry on a paper tissue inside the class 2 safety cabinet. To minimise movement and consequently damage to delicate structure of a hydrogel, the cubes were secured in place with glass coverslips  was mixed with 0.1 M sodium bicarbonate buffer  and 110 µL Cy5 NHS ester solution  added. The reaction mixture was stirred at 4 °C overnight. The reaction mixture was purified via the dialysis method at 4 °C to remove the unreacted dye and yield the Cy5 labelled collagen. It was then lyophilised and reconstituted in 20 mM acetic acid buffer.Corning 7007 Ultra-low attachment (ULA) 96-well round-bottom plates were used to culture the 3D spheroids. 80% confluent tdTomato MCF-7 monolayer cells were detached, collected and the cell number determined using an automated cell counter (Biorad TC20). A single-cell suspension was diluted in culture medium and cells seeded at 6000 cells/well to generate the spheroids . The plates were then centrifuged at 300 RCF for 5 min and cultured for 3 days until visible spheroid formation.N = 2) were placed in 4 μ-well glass bottom chambered coverslips  using a P1000 pipette with the pipette tip cut off at the end. Each spheroid was placed alone in 500 μL of phenol red-free culture media (1:1 DMEM:F12 supplemented with 10% FBS), ~5 mm away from the edge of a 10 mm beam splitter cube   Fig. S in each 6 cells/mL for Video SV6 cells/mL for Video SV5/mL for samples in both videos. The samples were kept at 37 °C and supplied with humidified 5% CO2 using cell culture incubators prior to imaging and then the Okolab stage-top incubator during the experiment.Time lapse videos were generated of MDA-MB231 cells expressing tdTomato fluorescent protein seeded in peptide gel and supplemented with unlabelled collagen type I  and bead probes . TrypLE  was used to detach cells from 2D culture at sub-confluence. The gels were prepared in ibidi 4-well chambered coverslips with a beam splitter cube inserted at one end, similar to the gel rheology experiments described above but with final cell seeding density increased to 2 × 10Single channel image volumes were acquired at 10 min time intervals with light sheet illumination at 532 nm using an automated LabVIEW programme on the OptoRheo. Acquired image volumes were subjected to 3D deconvolution using the Wiener Filter Preconditioned Landweber (WPL) method in the Parallel Iterative Deconvolution plugin in Fiji. The 4D videos were rendered in Imaris  and Amira 3D Pro (ThermoFisher Scientific).6 frames. The bead trajectories were analysed as described in the microrheology section above and depicted as spheres  in the videos by creating volume objects in a separate colour channel with a colour shade corresponding to the local measurement at the time.The time lapse videos were halted at regular intervals to acquire microrheology measurements  with repeated measures acquired at each probe on three consecutive days. Position of each sample condition in the chambered coverslip was randomised between repeats and the order of measurement for each sample condition was randomised between the three days to control for any potential effect of measurement order. The experiment was repeated in triplicate. The analysis was automated and batch processing was utilised as much as possible to remove any user bias.N = 2 spheroids were analysed with repeated measurements taken with probes at fixed distances from the edge of each spheroid .For the spheroid study, Further information on research design is available in the Supplementary MaterialsDescription of Additional Supplementary FilesSupplementary Video 1Supplementary Video 2Reporting Summary