Delivering mRNA to specific cell types remains challenging. Recent work opens the possibility of using synthetic RNA transfer pathways to engineer mRNA ‘delivery cells’. These cells could home to disease sites, locally produce and deliver mRNA to target cells, and thereby enable targeted editing, reprogramming or elimination of selected cells for therapeutic benefit.

The individual detection of human immunodeficiency virus (HIV) virions and resolution from extracellular vesicles (EVs) during analysis is a difficult challenge. Infectious enveloped virions and nonviral EVs are released simultaneously by HIV-infected host cells, in addition to hybrid viral EVs containing combinations of HIV and host components but lacking replicative ability. Complicating the issue, EVs and enveloped virions are both delimited by a lipid bilayer and share similar size and density. The feature that distinguishes infectious virions from host and hybrid EVs is the HIV genomic RNA (gRNA), which allows the virus to replicate. Single-particle analysis techniques, which provide snapshots of single biological nanoparticles, could resolve infectious virions from EVs. However, current single-particle analysis techniques focus mainly on protein detection, which fail to resolve hybrid EVs from infectious virions. A method to simultaneously detect viral protein and internal gRNA in the same particle would allow resolution of infectious HIV from EVs and noninfectious virions. Here, we introduce SPIRFISH, a high-throughput method for single-particle protein and RNA analysis, combining single particle interferometric reflectance imaging sensor with single-molecule fluorescence in situ hybridization. Using SPIRFISH, we detect HIV-1 envelope protein gp120 and genomic RNA within single infectious virions, allowing resolution against EV background and noninfectious virions. We further show that SPIRFISH can be used to detect specific RNAs within EVs. This may have major utility for EV therapeutics, which are increasingly focused on EV-mediated RNA delivery. SPIRFISH should enable single particle analysis of a broad class of RNA-containing nanoparticles.

Natural cell death pathways such as apoptosis and pyroptosis play dual roles: they eliminate harmful cells and modulate the immune system by dampening or stimulating inflammation. Synthetic protein circuits capable of triggering specific death programs in target cells could similarly remove harmful cells while appropriately modulating immune responses. However, cells actively influence their death modes in response to natural signals, making it challenging to control death modes. Here, we introduce naturally inspired “synpoptosis” circuits that proteolytically regulate engineered executioner proteins and mammalian cell death. These circuits direct cell death modes, respond to combinations of protease inputs, and selectively eliminate target cells. Furthermore, synpoptosis circuits can be transmitted intercellularly, offering a foundation for engineering synthetic killer cells that induce desired death programs in target cells without self-destruction. Together, these results lay the groundwork for programmable control of mammalian cell death.

Despite the vast diversity of the antibody repertoire, infected individuals often mount antibody responses to precisely the same epitopes within antigens. The immunological mechanisms underpinning this phenomenon remain unknown. By mapping 376 immunodominant “public epitopes” at high resolution and characterizing several of their cognate antibodies, we concluded that germline-encoded sequences in antibodies drive recurrent recognition. Systematic analysis of antibody-antigen structures uncovered 18 human and 21 partially overlapping mouse germline-encoded amino acid–binding (GRAB) motifs within heavy and light V gene segments that in case studies proved critical for public epitope recognition. GRAB motifs represent a fundamental component of the immune system’s architecture that promotes recognition of pathogens and leads to species-specific public antibody responses that can exert selective pressure on pathogens.

A system for programmable export of RNA molecules from living cells would enable both non-destructive monitoring of cell dynamics and engineering of cells capable of delivering executable RNA programs to other cells. We developed genetically encoded cellular RNA exporters, inspired by viruses, that efficiently package and secrete cargo RNA molecules from mammalian cells within protective nanoparticles. Exporting and sequencing RNA barcodes enabled non-destructive monitoring of cell population dynamics with clonal resolution. Further, by incorporating fusogens into the nanoparticles, we demonstrated the delivery, expression, and functional activity of exported mRNA in recipient cells. We term these systems COURIER (controlled output and uptake of RNA for interrogation, expression, and regulation). COURIER enables measurement of cell dynamics and establishes a foundation for hybrid cell and gene therapies based on cell-to-cell delivery of RNA.

We combined single-cell transcriptomics and lineage tracing to understand fate choice in human B cells. Using the antibody sequences of B cells, we tracked clones during in vitro differentiation. Clonal analysis revealed a subset of IgM+ B cells which were more proliferative than other B-cell types. Whereas the population of B cells adopted diverse states during differentiation, clones had a restricted set of fates available to them; there were two times more single-fate clones than expected given population-level cell-type diversity. This implicated a molecular memory of initial cell states that was propagated through differentiation. We then identified the genes which had strongest coherence within clones. These genes significantly overlapped known B-cell fate determination programs, suggesting the genes which determine cell identity are most robustly controlled on a clonal level. Persistent clonal identities were also observed in human plasma cells from bone marrow, indicating that these transcriptional programs maintain long-term cell identities in vivo. Thus, we show how cell-intrinsic fate bias influences differentiation outcomes in vitro and in vivo.

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

Lineage tracking offers a direct approach to study cell fate determination. In this work we combined single cell transcriptomics and lineage tracing to better understand fate-choice in human B cells. Using the antibody sequence to trace cell lineage during in vitro differentiation, we identified intrinsic proliferative and cell fate biases of B cell subtypes. Clonal analysis revealed that IgM memory B cells were more proliferative than any other B cell subtype, and that cells from the same clone had highly concordant fates. We found that transcriptional memory within clones varies across genes, with strongest persistence in genes related to cell fate determination. Similar persistent transcriptional programs were observed in human plasma cells from bone marrow, suggesting that these programs maintain long-term cell fate in vivo. These results show that cell-intrinsic fate bias influences human B cell differentiation and reveal molecular programs underpinning cell fate determination in B cells.

For more than 100 years, the fruit fly Drosophila melanogaster has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula Drosophilae, that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the Drosophila community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.

Neurons undergo substantial morphological and functional changes during development to form precise synaptic connections and acquire specific physiological properties. What are the underlying transcriptomic bases? Here, we obtained the single-cell transcriptomes of Drosophila olfactory projection neurons (PNs) at four developmental stages. We decoded the identity of 21 transcriptomic clusters corresponding to 20 PN types and developed methods to match transcriptomic clusters representing the same PN type across development. We discovered that PN transcriptomes reflect unique biological processes unfolding at each stage-neurite growth and pruning during metamorphosis at an early pupal stage; peaked transcriptomic diversity during olfactory circuit assembly at mid-pupal stages; and neuronal signaling in adults. At early developmental stages, PN types with adjacent birth order share similar transcriptomes. Together, our work reveals principles of cellular diversity during brain development and provides a resource for future studies of neural development in PNs and other neuronal types.

Recognition of environmental cues is essential for the survival of all organisms. Transcriptional changes occur to enable the generation and function of the neural circuits underlying sensory perception. To gain insight into these changes, we generated single-cell transcriptomes of Drosophila olfactory- (ORNs), thermo-, and hygro-sensory neurons at an early developmental and adult stage using single-cell and single-nucleus RNA sequencing. We discovered that ORNs maintain expression of the same olfactory receptors across development. Using receptor expression and computational approaches, we matched transcriptomic clusters corresponding to anatomically and physiologically defined neuron types across multiple developmental stages. We found that cell-type-specific transcriptomes partly reflected axon trajectory choices in development and sensory modality in adults. We uncovered stage-specific genes that could regulate the wiring and sensory responses of distinct ORN types. Collectively, our data reveal transcriptomic features of sensory neuron biology and provide a resource for future studies of their development and physiology.

Antibody memory protects humans from many diseases. Protective antibody memory responses require activation of transcriptional programs, cell proliferation, and production of antigen-specific antibodies, but how these aspects of the response are coordinated is poorly understood. We profile the molecular and cellular features of the antibody response to influenza vaccination by integrating single-cell transcriptomics, longitudinal antibody repertoire sequencing, and antibody binding measurements. Single-cell transcriptional profiling reveals a program of memory B cell activation characterized by CD11c and T-bet expression associated with clonal expansion and differentiation toward effector function. Vaccination elicits an antibody clone, which rapidly acquired broad high-affinity hemagglutinin binding during affinity maturation. Unexpectedly, many antibody clones elicited by vaccination do not bind vaccine, demonstrating non-specific activation of bystander antibodies by influenza vaccination. These results offer insight into how molecular recognition, transcriptional programs, and clonal proliferation are coordinated in the human B cell repertoire during memory recall.

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract A prerequisite for the design of an HIV vaccine that elicits protective antibodies is understanding the developmental pathways that result in desirable antibody features. The development of antibodies that mediate antibody-dependent cellular cytotoxicity (ADCC) is particularly relevant because such antibodies have been associated with HIV protection in humans. We reconstructed the developmental pathways of six human HIV-specific ADCC antibodies using longitudinal antibody sequencing data. Most of the inferred naive antibodies did not mediate detectable ADCC. Gain of antigen binding and ADCC function typically required mutations in complementarity determining regions of one or both chains. Enhancement of ADCC potency often required additional mutations in framework regions. Antigen binding affinity and ADCC activity were correlated, but affinity alone was not sufficient to predict ADCC potency. Thus, elicitation of broadly active ADCC antibodies may require mutations that enable high-affinity antigen recognition along with mutations that optimize factors contributing to functional ADCC activity. eLife digest Nearly four decades after the human immunodeficiency virus (HIV for short) was first identified, the search for a vaccine still continues. An effective immunisation would require elements that coax the human immune system into making HIV-specific antibodies – the proteins that can recognise, bind to and deactivate the virus. Crucially, antibodies can also help white blood cells to target and destroy cells infected with HIV. This ‘antibody-dependent cellular cytotoxicity’ could be a key element of a successful vaccine, yet it has received less attention than the ability for antibodies to directly neutralize the virus. In particular, it is still unclear how antibodies develop the ability to flag HIV-infected cells for killing. Indeed, over the course of an HIV infection, an immune cell goes through genetic changes that tweak the 3D structure of the antibodies it manufactures. This process can improve the antibodies' ability to fight off the virus, but it was still unclear how it would shape antibody-dependent cellular cytotoxicity. To investigate this question, Doepker et al. retraced how the genes coding for six antibody families changed over time in an HIV-carrying individual. This revealed that antibodies could not initially trigger antibody-dependent cellular cytotoxicity. The property emerged and improved thanks to two types of alterations in the genetic sequences. One set of changes increased how tightly the antibodies could bind to the virus, targeting sections of the antibodies that can often vary. The second set likely altered the 3D structure in others ways, potentially affecting how antibodies bind the virus or how they interact with components of the immune system that help to kill HIV-infected cells. These alterations took place in segments of the antibodies that undergo less change over time. Ultimately, the findings by Doepker et al. suggest that an efficient HIV vaccine may rely on helping antibodies to evolve so they can bind more tightly to the virus and trigger cellular cytotoxicity more strongly. Introduction A major concept underlying HIV vaccine research is that we can learn from the processes that lead to potent antibody responses in natural infection to inform immunogen design. For this reason, there have been several detailed studies of the evolutionary pathways of potent and broad neutralizing antibodies (bnAbs) to HIV (Bonsignori et al., 2017; Bonsignori et al., 2016; Doria-Rose et al., 2014; Landais et al., 2017; Liao et al., 2013; MacLeod et al., 2016; Simonich et al., 2019; Wu et al., 2015). These studies highlight the critical role of somatic hypermutation (SHM), particularly in complementary determining regions (CDRs) of antibody heavy chains, in driving the breadth and potency of HIV bnAbs (Kwong and Mascola, 2018). One recent study also highlighted that maturation of the antibody light chain, too, can be critical for breadth (Simonich et al., 2019). Collectively, these studies have provided important insights into the specific mutations that drive maturation of antibody lineages and specificities of bnAbs. The keen interest in HIV bnAbs stems in part from a plethora of compelling studies that demonstrate the efficacy of bnAbs in protection of macaques from experimental SHIV infection (Pegu et al., 2017). The data are less convincing for sterilizing protection by HIV-specific non-neutralizing antibodies that mediate antibody-dependent cell cytotoxicity (ADCC) in the same experimental animal models (Fouts et al., 2015), where the functional interactions of antibodies are harder to test due to species differences (Bournazos et al., 2017). However, they have been implicated in protection from HIV-infection in humans in several settings. In the only partially effective HIV vaccine trial to date, antibodies that mediate ADCC were associated with protection, whereas neutralizing antibodies were not (Haynes et al., 2012). Moreover, in the setting of mother-to-child transmission, ADCC antibodies have been associated with both decreased transmission risk and disease progression in infants (Mabuka et al., 2012; Milligan et al., 2015). For these reasons, ADCC antibodies, which have the potential to eliminate infected cells, have been increasingly recognized as important to consider for vaccine design and prevention efforts (Lewis et al., 2017). Yet, nothing is known about the process of SHM required for HIV specificity and ADCC function of antibodies. In light of the relevance of ADCC antibodies in human infections, we deep sequenced the antibody repertoire of an individual who developed potent ADCC antibodies to the HIV envelope (Env) gp120 and gp41 proteins. We used computational methods that were specifically developed to study antibody evolutionary pathways to infer the naïve precursors and resolve chronological lineage intermediates leading to six different mature ADCC-mediating IgG1 antibodies that have been previously described (Williams et al., 2015; Williams et al., 2019). Among the six lineages, most inferred naïve antibodies did not mediate ADCC. To develop ADCC functionality, inferred naive antibodies required CDR-localized SHM in one or both chains. In five of six cases, enhancement of ADCC potency to mature levels required mutations in framework regions (FWR), either alone or alongside additional CDR mutations. Interestingly, in one lineage, the developing antibodies first gained the capacity to bind HIV Env and then subsequently acquired ADCC activity due to additional SHM. Overall, binding affinity for Env and facilitation of ADCC were correlated, but we observed cases where binding affinity was similar amongst two antibodies within the same lineage but they differed in their ADCC capacity, suggesting that affinity alone is not sufficient for an antibody to mediate ADCC. Results Longitudinal sequencing of six ADCC HIV-1 antibody lineages We previously reported the isolation of six monoclonal antibodies (mAbs), each with potent antibody-dependent cell cytotoxicity (ADCC) activity, from a sample collected 914 days post-infection (D914) from a clade A-infected Kenyan woman (Williams et al., 2015; Williams et al., 2019). All these mAbs derived from distinct B cell lineages, with four targeting two distinct gp41 Env epitopes (006, 016, 067, 072), and the other two targeting distinct gp120 Env epitopes (105, 157). One of the gp120 targeting mAbs (105) also had modest capacity to neutralize cell-free virus (Williams et al., 2015), whereas the others were non-neutralizing. To infer the ontogeny of these six mAbs, we used next-generation sequencing (NGS) to sequence full-length antibody variable region genes from five longitudinal blood samples collected from the subject, spanning from pre-infection (D-119) to over 4 years post-infection (D1512). Amongst the different antibody chains and timepoints sequenced, the sequencing libraries ranged widely in their sample coverage, between 6 and 409% with an average of 100% coverage, based on estimated B cell frequencies within the available peripheral blood mononuclear cell (PBMC) samples (Table 1). It is important to note that, despite the name ‘deep sequencing’, NGS-sampled datasets are relatively shallow compared to the entire repertoire. This is for two reasons: (1) each 10 mL PBMC sample is only ~0.22% of the subject’s whole-body circulating blood volume (~4.5 L), not counting lymphoid organs or peripheral tissue and (2) circulating human memory B cell repertoires fluctuate over time (Horns et al., 2019; Laserson et al., 2014), making it difficult to accurately track clonal B cell families. Despite these limitations inherent to studies of this type, our sequencing efforts successfully returned clonal sequences for all six lineages of interest. Table 1 Longitudinal QA255 antibody variable region sequencing statistics. Time pointLive PBMC count (excluding non-viable)PBMC viabilityAb chainReplicateRaw MiSeq readsProductive replicate-merged deduplicated sequencesEstimated sequencing coverage within sampled blood PBMCs†Estimated sequencing coverage within QA255 d914 whole-body blood repertoire‡D-1191.39E+0784.22%IgM1486,458200,25421%0.05%IgG1604,237199,57048%0.11%IgK1705,163292,54321%0.05%IgL1406,267178,22113%0.03%D4621.40E+0786.42%IgG1646,809504,029120%0.22%2923,103IgK157,096506,98936%0.08%2967,693IgL1201,566402,45629%0.06%2586,568D7913.90E+0697.50%IgG1126,955263,848226%0.22%2776,5783*522,308IgK1527,031568,736146%0.22%2934,7803*1,057,984IgL147,368212,92255%0.12%2387,7303*537,611D11741.80E+06100.00%IgG1497,094220,740409%0.22%2665,9813*678,973IgK1254,890415,601231%0.22%2797,2393*929,145IgL1228,992307,415171%0.22%2453,9353*510,711D15121.00E+0779.37%IgG1370,198155,26252%0.12%IgK1408,498214,65121%0.05%IgL193,84855,7856%0.01%averages:293,689100%0.13% * Library replicate performed using unique molecular identifiers during cDNA synthesis step. † Sequencing coverage was calculated for PBMCs using QA255 D914 B cell frequency statistics from the cell sort (Williams et al., 2015). ‡ Whole-body sequencing coverage was calculated assuming 10 ml of blood was sampled from a total of 4500 ml adult blood volume. We identified sequences clonally related to the gamma heavy (VH) and lambda (VL) or kappa (VK) light chains of the six mature mAbs (Table 2; Figure 1A–B), inferred the most likely naive ancestors of each mAb chain, annotated clonal family V, (D), and J gene usage (Table 2), and calculated the degree of mutation in each lineage over time (Table 2; Figure 1C). Overall, the level of SHM in the mature D914 mAbs ranged from 3.3–11.5%, similar to our previous reporting of these lineages using different annotation methods (Williams et al., 2015; Williams et al., 2019). Incidentally, consensus among clonal sequences within families revealed potentially artifactual cloning mutations at the 5’ and 3’ ends of the variable regions in some of the previously characterized mature mAbs (Williams et al., 2015; Williams et al., 2019) and suggested, overall, that the consensus sequences were the most likely sequences for the antibodies. When directly compared, the sequence-corrected mAbs demonstrated equivalent epitope binding strength and ADCC activity to those previously studied (data not shown). Thus, we corrected the 5’ and 3’ ends of the mature D914 mAb variable regions to consensus sequences among each clonal family for use in this study (Supplementary file 1). Table 2 Clonal sequence analyses for ADCC antibody lineages. (A, B) Characteristics and statistics of heavy (A) and light (B) chain clonal families. Percent SHM was calculated as the mutation frequency at the nucleotide level compared to the predicted naïve allele, as determined by the per subject germline inference for QA255. Statistics calculated for individual timepoints within the context of the full subject repertoire were downsampled to 50–150K sequences for computational manageability. Light chain clonal family size statistics are reported but unreliable due to overclustering caveats that are explained in the Materials and methods. Per-timepoint clonal family statistics were excluded if ≤1 clonal heavy or light chain sequences were identified. #: The VH gene used in 067 and 157 lineages was determined to be an allele not cataloged in IMGT (http://www.imgt.org/): 1–69*11+A147G+C169Tl; the 157 lineage VK gene was also a new allele: 3–11*01+T5A+T9A+T36G+G84A. †: Percent SHM for 016 light chain lineage includes a 3-nt insertion in the CDRL3. SHM: somatic hypermutation; ints: intermediates. Longitudinal values for ‘No. of unique clonal sequences identified’ are plotted for heavy and light chains in Table 2; Figure 2—figure supplements 1, along with longitudinal values for ‘Average %SHM (nt)’ for heavy chains. Graphics displaying the most probable routes of antibody lineage maturation corresponding to the ‘No. of ints resolved in lineage path’ are available in Figure 4 and Figure 4—figure supplements 1–3. Heavy chainLineageMature antibody statisticsClonal family (all timepoints merged, not downsampled)Clonal family statistics within repertoire (downsampled analyses)No. ints resolved in lineage pathGene usage (partis)%SHM mature Ab# sampled clonal seqsAvg clonal family %SHMNo. unique clonal sequences identifiedSize: percent of sampled repertoireAverage %SHM (nt)VHDHJHD-119D462D791D1174D1512D-119D462D791D1174D1512D-119D462D791D1174D1512 0063–23*013–22*014*028.7710.8070000.002%7.61 0164–34*016–13*015*029.97029.20581112090.13%0.05%0.01%9.610.312.614 0671–69*11#1–1*016*038.13611.90351000.01%11.62 0721–69*114–17*013*0211.551512.5011118325610.04%0.05%0.17%0.04%9.512.813.015.15 1053–15*013–22*016*039.51934.901930000.04%52 1571–69*11#1–26*016*036.51837.406165840.004%0.08%0.04%0.03%9.37.710.311.84 Light chainLineageMature antibody statisticsClonal family (all timepoints merged, not downsampled)Clonal family statistics within repertoire (downsampled analyses)No. ints resolved in lineage pathGene usage (partis)%SHM mature Ab# sampled clonal seqsAvg clonal family %SHMNo. unique clonal sequences identifiedSize: percent of repertoireAverage %SHM (nt)VKVLJK/LD-119D462D791D1174D1512D-119D462D791D1174D1512D-119D462D791D1174D1512 0062–11*012*016.86736.11227427352620.14%7.17 0161–51*022*019.9†15187.3299339523321360.03%0.02%0.11%6.67.17.711 0672–11*013*023.320,3376.521496105340879467292.04%6.62 0721–27*011*017.827966.24767197025433560.17%0.18%0.18%0.25%6.36.36.67.05 1053–20*012*017.130,2868.03058488456071427124661.17%6.37 1573–11*015*015.0188.20120150.24%0.07%5.95.37 Figure 1 Download asset Open asset Longitudinal characteristics of heavy and light chain clonal families. (A, B) Number of clonal heavy (A) and light (B) chain sequences identified per sequenced timepoint per lineage. Caveats relating to light chain clusters are described in Methods. (C) Average variable region mutation for each clonal heavy chain family per timepoint. Dashed line at D914 indicates the timepoint of isolation of mature antibodies (Williams et al., 2015; Williams et al., 2019). SHM: somatic hypermutation. mAbs constructed from D462 NGS sequences mediated ADCC Each lineage varied in the time point(s) from which we identified clonal sequences, with most clonal VH sequences existing in the D462 datasets for four of the six lineages (Table 2A). The NGS sequences within each lineage that shared the highest nucleotide (nt) identity with the D914 mature VH variable regions, ranging from 88–99%, were found within the D462 or D791 timepoints (Supplementary file 2). As test cases, we synthesized antibodies with VH sequences derived from the D462 NGS results that were similar to the mature 006 and 016 mAbs (88.5% and 97.5% nt identity, respectively). The 006-NGSVH was paired with 006-NGSVL light chain that had 95.8% nt identity to the mature 006 VL (Figure 2—figure supplement 1A) and, since this option was not available for 016 VL, the 016-NGSVH was paired with a computationally-inferred light chain (016-2VL) that preceded the mature in development (Figure 2—figure supplement 1B). mAb functionalities were then assessed using the Rapid and Fluorometric ADCC assay, which uses primary PBMCs as target and effector cells (Gómez-Román et al., 2006); in this method, mAbs bind to antigen-coated target cells and facilitate lysis by effector cells. Both NGS-based mAbs demonstrated full ADCC function (Figure 2), suggesting that ADCC function likely developed within these lineages by D462 post-infection. Figure 2 with 1 supplement see all Download asset Open asset ADCC activity in antibodies made from NGS-sampled sequences. RFADCC activity of antibodies that use NGS-sampled heavy chains that are most similar in sequence to the mature 006 or 016 antibodies. The 006-NGSVH was paired with an NGS-sampled light chain; the 016-NGSVH was paired with a computationally-inferred mature-like sequence 016-2VL. mAb pairings and their mutations are illustrated in Figure 2—figure supplement 1, with sequences available in Supplementary file 1. Data reflect two independent experiments. Source data is available in Figure 2—source data 1. Figure 2—source data 1 Source data (both replicates) for RFADCC assessment of NGS mAbs, processed as detailed in Materials and methods. https://cdn.elifesciences.org/articles/63444/elife-63444-fig2-data1-v2.csv Download elife-63444-fig2-data1-v2.csv Inferred naive mAbs vary in antigen binding capability and ADCC function Inferred naïve ancestor mAbs across lineages varied in their ability to bind HIV Env and facilitate ADCC function. Of the gp41-targeted lineages, only the inferred naive precursor of the 072 lineage bound gp41 (KD = 34.4 nM) and mediated detectable ADCC, although the activity was very low (Figure 3A, C). Both naive mAbs from the gp120-targeted lineages bound monomeric gp120 (KD = 23.4 nM for 105-0VH0VK and 41.1 µM for 157-0VH0VK; Figure 3B). Interestingly, despite the 105 naïve mAb having higher binding affinity compared to 157 naïve mAb, it did not mediate ADCC, while the 157 naive mAb mediated potent ADCC (Figure 3C). Figure 3 with 1 supplement see all Download asset Open asset Inferred naïve mAbs from six lineages vary in antigen binding capability and ADCC function. (A–B) Binding kinetics of the inferred naive antibody (ligand) from each lineage to indicated concentrations of monomeric C.ZA.1197MB gp41 ectodomain (A) or BL035.W6M.C1 gp120 (B) (analyte). Best fitting lines (red) to a 1:1 binding model of ligand:analyte binding are shown. Data are representative of two independent experiments. (C) Positive control-normalized RFADCC activity of inferred naive antibodies compared to their respective mature antibodies. Normalization is described in Methods. Asterisks indicate indeterminate activity, as defined in Methods. Data are represented as mean ± SEM and reflect at least five independent experiments; source data are available in Figure 3—source data 1. 0: naive; m: mature. See Figure 3—figure supplement 1 for functional assessment of alternative naïve mAbs. Figure 3—source data 1 Source data (all replicates) for RFADCC assessment of inferred naive mAbs (Figure 3C), processed as detailed in Materials and methods. https://cdn.elifesciences.org/articles/63444/elife-63444-fig3-data1-v2.csv Download elife-63444-fig3-data1-v2.csv Typically, the uncertainty in the computational inference of antibody lineage naive precursors is largely ignored, even though single amino acid differences in the naive mAb can result in differences in HIV Env binding and functional properties (Yuan et al., 2011). To account for inference uncertainty, we also tested ‘alternative naive’ mAbs for each lineage that were inferred not by partis (Ralph and Matsen, 2016b), but by linearham (Dhar et al., 2020), a second computational method that jointly models V(D)J recombination and evolutionary history. For 7 of 12 antibody chains, this alternative approach generated naïve sequences that differed from the original naïve sequences by one to four amino acids within the CDR3 and/or FWR4 regions (Supplementary file 1). For the remaining five, the alternative method inferred the same naive sequence as the original method. When tested, the alternative naïve sequences did not alter antigen binding or function in five of six antibody lineages when compared to the original naïve mAbs (Figure 3—figure supplement 1A). However, in the 157 lineage we found that the alternative naïve VK chain (157-AVK) differed from the original naïve (157-0VK) by two adjacent amino acids in the CDRL3 (Figure 3—figure supplement 1D). When 157-AVK was paired with either the original (157-0VH) or alternative (157-AVH) VH chains, it abolished gp120 binding and ADCC functionality (Figure 3—figure supplement 1B,C). Thus, there is some uncertainty about whether the true 157 lineage naïve precursor was specific for HIV and/or capable of mediating ADCC. Characterization of lineage intermediates that confer HIV specificity and ADCC function To reconstruct probable developmental routes that led to the mature D914 mAb heavy and light chains, we used Bayesian phylogenetic lineage inference, which emphasizes only the inferred ancestral intermediates that have high relative statistical confidence and infers chronological ordering of these likely ancestral sequences. This method was specifically developed to reconstruct antibody evolution using sparse data (Simonich et al., 2019). Among the 12 heavy and light chain lineages for the six mAbs, our methods resolved anywhere from 1 to 14 probable intermediate sequences that lay between the naive and the mature sequences (Table 2). Figure 4, showing 105VK development, exemplifies one pathway, although all pathways may be viewed in Figure 4—figure supplements 1–3. Of note, 8 of 12 of these computationally-inferred lineages were validated by the existence of NGS-sampled sequences that were identical either in nucleotide or amino acid sequence to at least one of the inferred intermediates (Figure 4, Figure 4—figure supplements 1–3). Figure 4 with 3 supplements see all Download asset Open asset Example of most probable routes of antibody maturation: 105 light chain. Ancestral sequences (internal nodes within phylogenies) and their relative confidences were summarized for the 105 antibody light (kappa) chain from Bayesian clonal family phylogenies sampled from the associated posterior distribution for the 105-light clonal family of sampled NGS sequences. Results are displayed in this graphic illustrating multiple possible lineages of amino acid transitions and their relative confidences for 105 light chain development. Amino acid substitutions (arrows) connect the inferred naive sequence (white node, top) to the mature sequence (white node, bottom) via reconstructed ancestral intermediate sequences (red-shaded nodes). The red shading of nodes is proportional to the posterior probability that this ancestral sequence was present in the lineage. Low probability nodes were filtered out, resulting in some incomplete pathways within the graphics. For a given node, the blue shadings of multiple arrows arising from that node is proportional to the corresponding transition probability from that node to downstream nodes. Stars denote inferred sequences that were identical in nucleotide (green) or amino acid (yellow) sequence to sampled NGS sequences. ‘MID’ (red) denotes middle intermediate sequence. Labels with cyan highlighting denote sequences that were used in functionality assays (see also Supplementary file 1). See Figure 4—figure supplements 1–3 for probable maturation graphics for all 12 antibody chains of interest. To focus our subsequent lineage studies on determining which heavy chain and/or light chain mutations enabled HIV Env binding and ADCC gain-of-function, we employed the following strategy to select inferred intermediates of interest (detailed fully in Materials and methods). For each chain, we chose lineage intermediates with high relative confidence that were chronologically near the middle of the inferred lineage, paired them together (see Materials and methods), and performed preliminary experiments to determine whether antigen binding or ADCC gain-of-function occurred before or after these ‘middle’ intermediates (Figure 4, Figure 4—figure supplements 1–3). If the middle intermediate had function, we focused on pre-middle inferred intermediates, if available within the computationally-inferred lineages. If the middle intermediate lacked function, we focused on post-middle inferred intermediates. Selected heavy and light chain sequences, were paired together in all possible combinations, roughly chronologically (i.e. increasing SHM and within per-chain resolved chronologies), to reveal per-chain mutations contributing to mAb function. Because we did not use paired sequencing methods, these pairings do not necessarily reflect true biological intermediates, but, instead, allowed us to identify ordered steps within each chain’s maturation that conferred HIV binding and/or ADCC activity. For clarity, we are reporting only the pairings that revealed steps in gain-of-function for the six lineages. Pertinent mAbs thus defined the mutations that conferred HIV binding and ADCC activity in each lineage as follows: gp41 antibody 006: mAb 006 (VH3-23, VL2-11) targets a discontinuous epitope that includes the C-terminal heptad repeat (Williams et al., 2019). Mutations in CDRH2 and CDRH3, totaling 1.4% VH SHM, were minimally required for antigen binding and ADCC activity by 006-1VH0VL in the 006 lineage (Figure 5A). The addition of six light chain mutations among CDRL1, CDRL3, and FWRL2 augmented gp41 binding and ADCC potency in 006-1VH1VL. Figure 5 Download asset Open asset HIV specificity and ADCC development in gp41-targeted antibody lineages. (Left) Mature-normalized RFADCC activity of antibody lineage members for lineages 006 (A), 016 (B), 067 (C), and 072 (D), colored by strength of functionality: no function or indeterminate (gray), low function (<50% of mature activity; light blue), high function (>50% of mature activity; dark blue). Intermediate sequences are numbered consecutively based on their position in the developmental pathway between naïve (0) and mature (m). Asterisks indicate indeterminate activity, as defined in Methods. Data are represented as mean ± SEM and reflect at least four independent experiments, including data presented in Figure 3 for naive and mature Abs to best account for assay variability and to compare intermediates directly to these antibodies. Source data for all replicates are available in Figure 5—source data 1. (Middle) Binding of lineage members (ligand) to monomeric C.ZA.1197MB gp41 ectodomain (analyte, 62.5 nM), measured by BLI. Data are representative of two independent experiments. Data are colored based on each antibody’s RFADCC functionality. (Right) mAb heavy and light chain pairings illustrating variable region amino acid substitutions with respect to the reference sequence at the top of each lineage: black lines indicate amino acid substitutions; gray shaded regions demarcate CDRs. Figure 5—source data 1 Source data (all replicates) for RFADCC assessment of Ab lineages (Figures 5 and 6), processed as detailed in Methods. Duplicate columns accommodate for instances when two replicates were run on the same day. https://cdn.elifesciences.org/articles/63444/elife-63444-fig5-data1-v2.csv Download elife-63444-fig5-data1-v2.csv gp41 antibody 016: mAb 016 (VH4-34, VL1-51) targets a similar gp41 epitope as 006 (Williams et al., 2019). Detectable gp41 binding and ADCC function was demonstrated by the 016-1VH1VL mAb which contains mutations in heavy and light chain CDRs and FWRs (5.6% VH SHM and 3.6% VL SHM) (Figure 5B). A subsequent CDRL3 insertion of residue S94 (see details on chronology resolution of this insertion in Methods) allowed for augmented ADCC capacity (016-1VH2VL). Additional FWR and CDR mutations in the VH chain (016-2VH2VL) further strengthened gp41 binding and ADCC functionality. gp41 antibody 067: mAb 067 (VH1-69, VL2-11) targets the C-C’ loop (Williams et al., 2019). For the 067 lineage, the 067-0VH1VL mAb incorporated a single CDRL3 mutation (0.3% VL SHM) and displayed detectable antigen binding; we could not determine whether there was meaningful ADCC function or not as levels were just above background (Figure 5C). To achieve more convincing ADCC function, two CDRH2 mutations were also required (1.0% VH SHM). Further mutations in either the heavy or light chain FWRs modestly increased ADCC potency to >50% that of the 067 mature mAb. gp41 antibody 072: mAb 072 (VH1-69, VK1-27) targets an overlapping but distinct epitope as 067 (Williams et al., 2019). Unlike the other gp41-targeting lineages, the 072 lineage inferred naive mAb bound gp41 antigen and mediated weak ADCC, as aforementioned (Figures 3A,C and 5D). To gain ADCC potency, only mutations in the VH chain were necessary; mAbs 072-1VH0VK (0.8% VH SHM) and 072-2VH0VK (0.5% VH SHM) demonstrate that two substitutions in either the CDRH1 or CDRH2 increased activity, but, when combined in 072-3VH0VK, they were not additive or synergistic (Figure 5D). Instead, FWRH3 and FWRH4 mutations in mAb 072-4VH0VK were required for ADCC activity comparable to that of 072 mature mAb. gp120 antibody 105: mAb 105 (VH3-15, VK3-20) targets a V3 linear epitope (Williams et al., 2015). The inferred 105 lineage naive and 105-0VH1VK mAbs bo

Antibodies function by binding to antigens. Antibodies must be cloned and expressed to determine their binding characteristics, but current methods for high-throughput antibody sequencing yield antibody DNA pooled from many cells and do not readily permit cloning of antibodies from single B cells. We present a strategy for retrieving and cloning antibody DNA from single cells within a pooled library of cells. Our strategy, called selective PCR for antibody retrieval (SPAR), takes advantage of the unique sequence barcodes attached to individual cDNA molecules during sample preparation to enable specific amplification by PCR of antibody heavy- and light-chain cDNA originating from a single cell. We show through computational analysis that most human antibodies sequenced using typical high-throughput methods can be retrieved using SPAR, and experimentally demonstrate retrieval of full-length antibody variable region cDNA from three cells within pools of ~5,000 cells. SPAR enables rapid low-cost cloning and expression of native human antibodies from pooled single-cell sequence libraries for functional characterization.

Antibodies are created and refined by somatic evolution in B cell populations, which endows the human immune system with the ability to recognize and eliminate diverse pathogens. However, the evolutionary processes that sculpt antibody repertoires remain poorly understood. Here, using an unbiased repertoire-scale approach, we show that the population genetic signatures of evolution are evident in human B cell lineages and reveal how antibodies evolve somatically. We measured the dynamics and genetic diversity of B cell responses in five adults longitudinally before and after influenza vaccination using high-throughput antibody repertoire sequencing. We identified vaccine-responsive B cell lineages that carry signatures of selective sweeps driven by positive selection, and discovered that they often display evidence for selective sweeps favoring multiple subclones. We also found persistent B cell lineages that exhibit stable population dynamics and carry signatures of neutral drift. By exploiting the relationship between B cell fitness and antibody binding affinity, we demonstrate the potential for using phylogenetic approaches to identify antibodies with high binding affinity. This quantitative characterization reveals that antibody repertoires are shaped by an unexpectedly broad spectrum of evolutionary processes and shows how signatures of evolutionary history can be harnessed for antibody discovery and engineering.

The ultimate function of a neuron is determined by both its physiology and connectivity, but the transcriptional regulatory mechanisms that coordinate these two features are not well understood1-4. The Drosophila olfactory receptor neurons (ORNs) provide an excellent system to investigate this question. As in mammals5, each Drosophila ORN class is defined by the expression of a single olfactory receptor or a unique combination thereof, which determines their odor responses, and by the single glomerulus to which their axons target, which determines how sensory signals are represented in the brain6-10. In mammals, the coordination of olfactory receptor expression and wiring specificity is accomplished in part by olfactory receptors themselves regulating ORN wiring specificity11-13. However, Drosophila olfactory receptors do not instruct axon targeting6, 14, raising the question as to how receptor expression and wiring specificity are coordinated. Using single-cell RNA-sequencing and genetic analysis, we identified 33 transcriptomic clusters for fly ORNs. We unambiguously mapped 17 to glomerular classes, demonstrating that transcriptomic clusters correspond well with anatomically and physiologically defined ORN classes. We found that each ORN expresses ~150 transcription factors (TFs), and identified a master TF that regulates both olfactory receptor expression and wiring specificity. A second TF plays distinct roles, regulating only receptor expression in one class and only wiring in another. Thus, fly ORNs utilize diverse transcriptional strategies to coordinate physiology and connectivity.

The regulatory mechanisms by which neurons coordinate their physiology and connectivity are not well understood. The Drosophila olfactory receptor neurons (ORNs) provide an excellent system to investigate this question. Each ORN type expresses a unique olfactory receptor, or a combination thereof, and sends their axons to a stereotyped glomerulus. Using single-cell RNA sequencing, we identified 33 transcriptomic clusters for ORNs and mapped 20 to their glomerular types, demonstrating that transcriptomic clusters correspond well with anatomically and physiologically defined ORN types. Each ORN type expresses hundreds of transcription factors. Transcriptome-instructed genetic analyses revealed that (1) one broadly expressed transcription factor (Acj6) only regulates olfactory receptor expression in one ORN type and only wiring specificity in another type, (2) one type-restricted transcription factor (Forkhead) only regulates receptor expression, and (3) another type-restricted transcription factor (Unplugged) regulates both events. Thus, ORNs utilize diverse strategies and complex regulatory networks to coordinate their physiology and connectivity.

How a neuronal cell type is defined and how this relates to its transcriptome are still open questions. The Drosophila olfactory projection neurons (PNs) are among the best-characterized neuronal types: Different PN classes target dendrites to distinct olfactory glomeruli and PNs of the same class exhibit indistinguishable anatomical and physiological properties. Using single-cell RNA-sequencing, we comprehensively characterized the transcriptomes of 40 PN classes and unequivocally identified transcriptomes for 6 classes. We found a new lineage-specific transcription factor that instructs PN dendrite targeting. Transcriptomes of closely related PN classes exhibit the largest difference during circuit assembly, but become indistinguishable in adults, suggesting that neuronal subtype diversity peaks during development. Genes encoding transcription factors and cell-surface molecules are the most differentially expressed, indicating their central roles in specifying neuronal identity. Finally, we show that PNs use highly redundant combinatorial molecular codes to distinguish subtypes, enabling robust specification of cell identity and circuit assembly.

A key feature of cells is the capacity to activate new functional polarized domains contemporaneously to pre-existing ones. How cells accomplish this is not clear. Here, we show that in fission yeast inhibition of cell polarity at pre-existing domains of polarized cell growth is required to activate new growth. This inhibition is mediated by the ERM-related polarity factor Tea3, which antagonizes the activation of the Rho-GTPase Cdc42 by its co-factor Scd2. We demonstrate that Tea3 acts in a phosphorylation-dependent manner controlled by the PAK kinase Shk1 and that, like Scd2, Tea3 is direct substrate of Shk1. Importantly, we show that Tea3 and Scd2 compete for their binding to Shk1 in vitro, indicating that their biochemical competition for Shk1 underpins their antagonistic roles in controlling polarity. Mathematical modelling of an 9inhibitor-activator9 system mimicking the Tea3-Scd2 antagonism suggests that it can account for several major features of polarity control observed in cells, including the anti-correlated localizations of Tea3 and cell growth areas observed throughout the cell cycle, controlling the period of GTP-Cdc42 oscillations at the cell cortex and modulating the time at which cells trigger new polarized growth. Thus, by preventing pre-existing growth domains from becoming overpowering, polarity inhibition plays a key role in allowing cells to redistribute their polarity-activating machinery to prospective growth sites and control the timing of new growth activation - explaining how a polarity inhibitor can serve as an activator of new polarity zone formation.

The definition of neuronal type and how it relates to the transcriptome are open questions. Drosophila olfactory projection neurons (PNs) are among the best-characterized neuronal types: different PN classes target dendrites to distinct olfactory glomeruli, while PNs of the same class exhibit indistinguishable anatomical and physiological properties. Using single-cell RNA sequencing, we comprehensively characterized the transcriptomes of most PN classes and unequivocally mapped transcriptomes to specific olfactory function for six classes. Transcriptomes of closely related PN classes exhibit the largest differences during circuit assembly but become indistinguishable in adults, suggesting that neuronal subtype diversity peaks during development. Transcription factors and cell-surface molecules are the most differentially expressed genes between classes and are highly informative in encoding cell identity, enabling us to identify a new lineage-specific transcription factor that instructs PN dendrite targeting. These findings establish that neuronal transcriptomic identity corresponds with anatomical and physiological identity defined by connectivity and function.