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Take, for example, the pint-sized projector we’re going to review today: the PVO YG300 Mini Projector. This projector is made first and foremost for portability, and it’s as compact and lightweight as they come. But can good things really come in small packages? To find out, we’ll need to dig a little deeper into how it functions, and what it’s capable of. We’ll start by reviewing the physical design. We’ll talk about how it’s constructed, and how the controls work. Next, we’ll look at the quality of the video itself, as well as the built-in speakers. Last, we’ll talk about how you can connect to the projector, and any extra features it offers. At that point, we’ll have a more complete picture. Let’s begin!

The PVO YG300 Mini Projector is a tiny projector that measures 5.6 inches wide, 3.7 deep, and 2.1 thick. That’s about the size of a paperback book, which is truly remarkable for a fully-functional projector. It weighs only 11.2 ounces, which is fairly light, even for the small size. This makes it very easy to carry around. You can drop it in your laptop bag or backpack and you’ll hardly even notice you’re carrying it. The case has a two-tone design, with orange-gold on the right third and off white on the rest. There’s also an off white PVO logo on the top, directly over the lens.

All in all, the PVO YG300 Mini Projector is a bit of a mixed bag. Its main benefit is its portability – you can take it anywhere you want, even on an airplane. It’s pretty much unparalleled in that regard. On the downside, the quality is less than we’d expect from a modern projector. Even if it were 720p, that would be a major improvement.

Taking advantage of the increased dimensionality of mass cytometry, we prepared a set of reagents to capture a system-wide view of immune cell types from a replicate analysis of bone marrow mononuclear cells from two healthy human donors. Thirty-one distinct transition element isotopes were used to label two antibody-staining panels for the study of healthy human bone marrow mononuclear cells. [Data are publicly available at Cytobank (www.cytobank.org/nolanlab). An “immunophenotyping” panel was designed that monitored 13 “core” surface markers and 18 subset-specific cell-surface markers to allow identification of human hematologic cell types. A “functional” panel contained the 13 core surface markers and also 18 intracellular epitopes that reflect intracellular signaling states, such as phosphorylation status of kinase substrates (21). These complementary panels allowed simultaneous biochemical analysis of intracellular signaling in rare and diverse cell subsets that were identified through in silico merging of the data. Intracellular signaling responses were determined by treating cells ex vivo with modulators such as cytokines, small molecules, or combinations thereof. Perturbation analysis has proven useful in causality determinations for signaling at the single-cell level (11, 22-25) and was applied here to enable cell subset–specific response profiles. An additional three parameters—a DNA intercalator, cell length, and a cell viability dye (21)—were included in the analysis panels, creating a total of 34 parameters in each. With an overlap of 13 core surface antibodies between the two analysis panels and the three shared additional cell features, a combined total of 52 unique single-cell parameters were measured. The resulting single-cell data set of bone marrow cells captured a snapshot of the cell types present and their corresponding regulatory signaling responses throughout development from early human hematopoietic progenitors to lineage-committed cells.

We hypothesized that the inherent similarity of cell stages and continuity of the transitions between cell differentiation states could be used to organize high-dimensional data into ordered, continuous clusters of similar cell phenotypes that, when projected on a two-dimensional (2D) plane, would convey the relatedness of these cells in a higher dimensional space. We leveraged progressive changes in CD marker expression to organize bone marrow cells in an unsupervised manner, creating a tree-like scaffold for visualization of high-dimensional intracellular signaling behaviors in various cell types present during hematopoietic development in the bone marrow (27, 28). To accomplish this, we used SPADE (spanning-tree progression analysis of density-normalized events), a density normalization, agglomerative clustering, and minimum-spanning tree algorithm to distill multidimensional single-cell data down to interconnected clusters of rare, transitional, and abundant cell populations, which were organized and displayed as a 2D tree plot (Fig. 2A). Such a tree plot from healthy bone marrow represented the clustered expression of the cell-surface antigens that were used to build the tree in 13-dimensional space on the basis of the core surface markers conserved between our two 34-parameter analysis panels (CD3, -4, -8, -11b, -19, -20, -33, -34, -38, -45, -45RA, -90, and -123) (Fig. 2B). Each node of the plot encompasses a cluster of cells that were phenotypically similar in the 13-dimensional space defined by the core surface markers. The approach uses a minimum-spanning tree algorithm, in which each node of cells is connected to its “most related” node of cells as a means to convey the relationships between the cell clusters. The number of nodes and ultimately their boundaries is driven by a user-definable value (21). Each node describes an n-dimensional boundary encompassing a population of phenotypically similar cells. When connected via the minimum spanning tree, this provides a convenient approach to map complex n-dimensional relationships into a representative 2D structure.

SPADE links related immune cell types in a multidimensional continuum of marker expression. (A) Summary of SPADE analysis. Single-cell data are sampled in a density-dependent fashion so as to reduce the total cell count while maintaining representation of all cell phenotypes. Neighboring cells are then grouped by unsupervised hierarchical clustering. Resulting nodes (defined as those cells within a boundary of an n-dimensional hull) are then linked by a minimum-spanning tree, which is flattened for 2D display.(B) Immunophenotypic progression in healthy human bone marrow. A tree plot was constructed by using 13 cell-surface antigens in healthy human bone marrow. 18 additional intracellular parameters were acquired concurrently but excluded from tree construction. The size of each circle in the tree indicates relative frequency of cells that fall within the 13-dimensional confines of the node boundaries. Node color is scaled to the median intensity of marker expression of the cells within each node, expressed as a percentage of the maximum value in the data set (CD45RA is shown). Putative cell populations were annotated manually (table S2) and are represented by colored lines encircling sets of nodes that have CD marker expression emblematic of the indicated subset designations. (C) Overlaid expression patterns of CD3, CD8, and CD4. Three markers, along with CD45RA (B), were used in clustering that helped define T cell lineages. Color scale is as described in (B). (D) Overlaid expression patterns of CD19, CD20, and CD38. Three markers were used in clustering that helped define B cell lineages. Color scale is as described in (B). (E) Overlaid expression patterns of CD34, CD123, and CD33. Three markers were used in clustering that helped define myeloid and progenitor cell lineages. Color scale is as described in (B). (F) Overlaid expression of complementary surface markers from a staining panel with 18 additional surface markers (fig. S4) by using the 13 core surface markers as landmarks (21). Overlaid expression patterns are shown for eight complementary surface markers that helped to further define the myeloid (CD13, CD14, and CD15), B cell (CD10), and NK/T cell (CD7, CD56, CD161, and CD16) portions of the SPADE representation. These markers were not used for tree construction. Color scale is as described in (B).

Although they were not used in the tree-building step, the 18 additional surface markers from the “immunophenotype”-staining panel were used to confirm and refine cell subset annotations (Fig. 2F and fig. S4B). These markers were overlaid in an unsupervised fashion onto the existing tree by assigning each cell from the immunophenotyping experiment to whichever node contained analogous cells from the functional data set according to the expression of the shared 13 core surface markers in the registration space. The accuracy of this automated overlaying approach is supported by the agreement of multiple natural killer (NK), monocyte, and B cell markers that localized to the appropriate cell populations (Fig. 2F), even though they were not used to direct the tree’s original organization. Although the tree structure derived from bone marrow data recapitulates many features of hematopoietic organization and relatedness, it is interpreted here as a map of the phenotypic relationships between diverse cell types and is not meant to imply a developmental hierarchy. Indeed, even measuring a large number of cells in a single tissue will fail to capture some developmental transitions, including (i) rapid activation (release of cytoplasmically sequestered receptors) (29); (ii) uneven surface marker partitioning during asymmetric cell division (30); and (iii) organ-specific development outside the assayed organ (maturation of T cells in the thymus). In this bone marrow data set, several well-defined cell types (such as T, NK, B, and monocyte) provide landmarks for the organization of the tree and give context to the nodes encompassing transitional and less-understood cell types. Ultimately, this approach enabled visualization of 34-dimensional bone marrow data in an intuitive graphical format. Although the algorithm over-segregated some cell types into redundant contiguous clusters, this approach has several advantages that complemented the complexity of this data set: (i) increased resolution captured unexpected and transitional cell types that escape standard classification strategies; (ii) Unsupervised analysis helped overcome the bias of subjective gating; and (iii) n-dimensional algorithms leveraged the multi-parameter mass cytometry data to define cell types on the basis of previously unappreciated, subtle differences in surface expression. Although we used stochastically selected “seed” cells to initiate the tree generation along with local similarity clustering and minimum-spanning trees, the approach is amenable to incorporation of other more deterministic partitioning approaches that might allow for other standardized tree structure formation.

Signaling functions mark developmental transitions in hematopoietic progression. (A) A heatmap summary, ordered developmentally by cell type and stimulation condition, of the status of 18 intracellular functional markers in cells treated with 1 of 13 biological and chemical stimuli. (Left) Abbreviations refer to recombinant human proteins, except BCR, B cell receptor cross-linking; LPS, lipopolysaccharide; PMA/Iono, phorbol-12-myristate-13-acetate with ionomycin; and PVO4, pervanadate. Single-cell data from healthy human bone marrow were manually divided (“gated”) into 24 conventional cell populations (fig. S5) according to 13 surface markers and DNA content. Signaling induction was calculated as the difference of inverse hyperbolic sine (arcsinh) medians of the indicated ex vivo stimulus compared with the untreated control for each manually assigned cell type (21). Each row within a given stimulus group (gray bars) indicates the signaling induction of 1 of 18 intracellular functional markers (bottom). A subset of conditions (red numbers) was highlighted for further discussion in (B). (B) Magnified view of the conditions marked in (A). A subset of these signaling responses (blue boxes) are shown as SPADE plots [(C) to (E)] to investigate correlations between signaling function and differences in immunophenotype as discussed in the text. (C) Canonical, cell type–specific signaling functions. Stimulation by IL-7, BCR, or LPS each induced phosphorylation of STAT5 in T cells, BLNK (SLP-65) [detected with an antibody raised against pSLP-76 (21)] in B cells, and p38 MAPK in monocytes, respectively. Signaling induction for each node in the SPADE diagram was calculated as the difference of arcsinh median intensity of the indicated ex vivo stimulus compared with the untreated control. (D) Correlation of IL-3–mediated induction of pSTAT3 and pSTAT5 with IL-3Ra expression [(Left) color scale as described in Fig. 2B] in myeloid and B cells. The B cell population that did not phosphorylate STAT3 in response to IL-3 stimulation is indicated (blue arrow). All nucleated cell subsets, including IL-3Rα+ B cells, exhibited pSTAT3 induction in response to IFNα stimulation. Signaling induction calculated as in (C). (E) Examples of phosphorylation responses that paralleled immunophenotypic progression identified by the SPADE algorithm. Changes in Btk/Itk, S6, CREB phosphorylation, and total IκBα are shown in response to IFNα, G-CSF, PVO4, and TNFα, respectively. Signaling induction is calculated as in (C).

For a more objective and fine-grained view of these cell type–specific responses, free of the biases of conventional 1D and 2D surface marker categorization, we overlaid the signaling behavior of the 18 functional epitopes on the tree structure using a similar approach as described for the immunophenotype staining panel (Fig. 2), allowing the intracellular signaling status to be visualized on the previously annotated tree structure (Fig. 3C). Nodes were colored according to the magnitude of the difference in their median responses relative to the untreated control. This effectively eliminated the subjectivity of manual classification and improved the resolution of the heatmap (Fig. 3A), separating the 24 manually assigned cell types into 282 logically connected nodes of phenotypically distinct, but locally similar, cell clusters.

Other responses, such as phosphorylation of the protein tyrosine kinases Btk and Itk mediated by IFNα or ribosomal protein S6 by G-CSF, were less tightly confined, exhibiting a range of activity that spanned multiple cell types (Fig. 3E). Yet other responses showed a signaling “gradient,” as exemplified by pervanadate (PVO4)–mediated disruption of the kinase/phosphatase balance upstream of the adenosine 3′,5′-monophosphate (cAMP) response element–binding protein (CREB) transcription factor. A gradient of responses, highest in HSCs, decreased gradually along the path of B cell maturation (Fig. 3E). A range of NFκB signaling responses, as measured by monitoring total IκBα levels, were observed across monocyte, NK and T cell subsets following TNFα stimulation (Fig. 3E, light blue nodes). As in the CREB response to PVO4 described above (Fig. 3E), the consistency of responses within the different T cell subsets suggests tightly regulated differences in signaling molecules that underlie the discrete functional roles of these related cell types. Together, these varied signaling responses across algorithmically defined partitions dictated solely by surface marker immunophenotype imply the existence of different classes of developmental transition points: (i) precise transitions, which are characterized by coordinated changes in cell signaling, such as the IL-7/pSTAT5 response in T cells and the LPS/p-p38 response in monocytes (Fig. 3C), and (ii) continuous developmental progressions, which are characterized by gradual gain or loss of expression of certain kinases or phosphatases, as highlighted by PVO4/pCREB (Fig. 3E) in B cells (28). The latter is indicative of fine-grained changes in regulatory architecture that track with immuno-phenotype within conventionally defined hema-topoietic compartments and provides an opportunity to explore the mechanisms that define these distinctive regulatory phenomena.

PCA confirms that cellular signaling potentially tracks with the immunophenotypic continuum in B cell subsets. (A) Using the SPADE representation (right), cells assigned to pre-B II, immature B, mature B, and IL-3Rα+ mature B cell populations were selected for PCA in 13 dimensions defined by the core immunophenotypic markers used in both panels. The relative frequencies of the four B cell populations are shown as stacked bars in 1% windows along the phenotypic progression axis (colors correspond to key at right); the number of cells in each window is expressed as a proportion of the sample subjected to PCA (gray line). (B) The measured intensities of five immunophenotypic markers (CD45RA, CD19, CD20, CD38, and CD123) along the phenotypic progression axis. These markers captured the majority of the phenotypic changes observed here during B cell maturation. Intracellular Ki67 expression, an indicator of cellular proliferation, was not used in defining the PCA axis but was among the 18 functional markers that were measured concurrently at thesingle-cell level.(C) Phosphorylation of ERK1/2, SLP-76(BLNK/SLP-65), PLCγ2, CREB, and SHP2 overlaid on the PCA progression axis. These and other functional epitopes were not used in the PCA axis construction. The top plot displays the basal levels (untreated) of these phosphorylated epitopes in the untreated sample. Subsequent plots display induced changes in phosphorylation in response to PVO4 and B cell receptor cross-linking relative to the untreated control.

Overlaying the basal (untreated) intensities of phosphorylated extracellular signal–regulated kinase 1 and 2 (pERK1/2), Src homology protein tyrosine phosphatase 2 (SHP2), SLP-76/BLNK (SLP-65), pPLCγ2, and CREB onto the PCA axis revealed that basal phosphorylation of these molecules at the measured sites was relatively constant across differentiating B cell types (Fig. 4C). However, induced phosphorylation of these sites by PVO4 and BCR (Fig. 4C) clarified the heterogeneity of PLCγ2 phosphorylation observed after PVO4 treatment in the tree plots (Fig. 5B), revealing instead a gradual decline that tracked with maturation. An opposing trend was observed in pPLCγ2 responsiveness to BCR activation, which exhibited an increase that closely tracked with CD20 expression (Fig. 4C). The same trend was observed for pCREB pERK1/2, pSLP-76, and pSHP2, suggesting a parallel and coordinated change in signaling pathways. In contrast, mature B cells lacked a PVO4-sensitive mechanism, but the same set of signaling mediators appeared to be repurposed in a coordinated phosphorylation response to BCR activation (Fig. 4C). These results suggest that in pre-B II and immature B cells, but not mature B cells, pPLCγ2 activation is increased by an upstream tyrosine kinase with tightly regulated activity. This example supports the existence of parallel signaling mechanisms affecting these nodes (SLP-76, SHP2, PLCγ2, and ERK1/2) that gradually switches along with the expression continuums determined by external immunophenotypic markers. Together, these parallel continuums could define each cell’s functional role within the greater hematopoietic system.

Multiplexed mass cytometry analysis reveals diverse signaling dynamics in response to the kinase inhibitor agent dasatinib. (A) SPADE plots of exemplary cell type–specific inhibitory effects of dasatinib. Phosphorylation of ERK1/2 was sensitive to dasatinib when induced with BCR cross-linking but not when induced with PMA/ionomycin. Signaling induction for each node in the SPADE diagram was calculated as the difference of arcsinh median of the indicated ex vivo stimulus compared with the untreated control. (B) T lymphocytes exhibited STAT5 phosphorylation in response to IL-7 in the presence of dasatinib with similar magnitudes as the response observed without drug. PVO4 induction of STAT5 phosphorylation was inhibited by dasatinib in all but plasmacytoid dendritic cells. Calculated as in (A). (C) Blymphocytes exhibited specific PLCγ2 phosphorylation in response to receptor cross-linking that was completely abolished in the presence of dasatinib. PLCγ2 phosphorylation was relatively large in all but B lymphocyte lineages in the presence of PVO4 but was inhibited completely by dasatinib treament in all cells. Calculated as in (A). (D) Using the SPADE representation, cells corresponding to HSC, MPP, pro-B, and pre-B I cell populations were selected for PCA of the 13 core immunophenotypic markers. The relative frequencies of the four progenitor cell populations are shown as stacked bars in 1% windows along the phenotypic progression axis (colors correspond to key at right); the number of cells in each window is expressed as a proportion of the sample subjected to PCA (gray line). (E) The measured intensities of six immunophenotypic markers (CD34, CD33, CD19, CD20, CD38, and CD123) along the progression axis. These markers captured the majority of the phenotypic changes observed here during progenitor cell maturation. (F) Basal (untreated) phosphorylation levels of p38, SrcFK, CREB, and SHP2 overlaid on the phenotypic progression axis. These and other functional epitopes were not used in the PCA axis construction. (G) Induced changes in phosphorylation of p38, SrcFK, CREB, and SHP2 in response to PVO4 compared with untreated control. Signaling induction is calculated as in (A). (H) Suppression of normal PVO4 response by dasatinib. Suppression index is calculated as the signaling induction by PVO4 with dasatinib pretreatment, minus the signaling induction by PVO4 alone.

Having established a baseline of healthy signaling responses to a panel of stimuli, we examined cell type–specific pharmacologic effects of some well-characterized kinase inhibitors. These included the Janus kinase (JAK) I inhibitor and MAPK kinase (MEK) inhibitor U0126. Predictably, when combined respectively with G-CSF and PMA/Ionomycin treatments of human BM (figs. S8 and S9) reliable and specific inhibition, respectively, of STAT3 and ERK1/2 phosphorylation are observed, which is consistent with previously reported observations that used conventional single-cell analysis platforms (44). Although interesting results were obtained with these inhibitors, we expanded to focus on dasatinib, a clinically relevant small-molecule kinase inhibitor. Dasatinib was originally introduced as a second-line BCR-ABL kinase inhibitor for imatinib-resistant chronic myelogenous leukemia (CML) (45). Unlike imatinib, dasatinib is estimated to inhibit over 100 kinases besides Abl—particularly, Src family kinases (SrcFKs) (46). This promiscuity is credited for dasatinib’s therapeutic efficacy in other malignancies (47). However, both malignant and healthy cells must integrate the effects of a drug with myriad other environmental inputs. We postulated that assessing drug activity in the presence of ex vivo stimuli may reveal interactions that underlie side effects for patients or expose new opportunities for pathway intervention.

Using the same healthy human bone marrow, we selected a panel of ex vivo stimuli that induced signaling in cell subsets either broadly [phorbol 12-myristate 13-acetate (PMA)/ionomycin and PVO4] or specifically (Flt3-L, IL-7, and BCR), after 30 min of pretreatment with dasatinib. The results showed several examples of pathway-specific inhibition that fit with expected roles of dasatinib (fig. S10). For instance, activation of pERK1/2 in immature and mature B cells through BCR cross-linking was completely suppressed by dasatinib (Fig. 5A), most likely through inhibition of Lyn, a critical SrcFK downstream of the B cell receptor (48). In contrast, PMA/ionomycin–mediated activation of pERK1/2 was unaffected by dasatinib, thus confirming the observation that protein kinase C (PKC) signaling is mediated by dasatinib-insensitive kinases (Fig. 5A) (49).

Phosphorylation patterns of PLCγ2 after PVO4 induction (Fig. 5B) were similar to PMA/ionomycin induction of pERK1/2 in the absence of dasatinib. However, dasatinib had a uniformly suppressive effect upon PVO4 induction of PLCγ2 in all cell types (Fig. 5B), whereas it minimally inhibited induction of Erk1/2 upon PMA/ionomycin stimulation (Fig. 5A). Thus, in contrast to the dasatinib-insensitive PKC pathway described above (Fig.5A) the PVO4-sensitive cascade upstream of PLCγ2 was inhibited by dasatinib in all cell types, including platelets. This result may reconcile a clinical observation of platelet dysfunction in CML patients treated with dasatinib (50), in which the inhibition of the SFKs upstream of PLCγ2 (Lyn and Fyn) is the proposed mechanism of dysregulation.

In B cells, dasatinib prevented phosphorylation of all measured components of the BCR signaling cascade (Syk, SHP2, Btk, BLNK, and PLCγ2) regardless of whether activation was through BCR crosslinking or PVO4 treatment (Fig. 5B and fig. S8C). This off-target activity may underlie the efficacy reported in a patient with chronic lymphocytic leukemia, a B cell malignancy (51). However, these effects may also have undesirable consequences. For example, suppression of subtle pro-survival (“tonic”) B cell signaling (52) may account for the decline of circulating B cells observed in CML patients undergoing high-dose dasatinib therapy (53).

Disruption of the tyrosine kinase/phosphatase equilibrium with PVO4 also caused potent phosphorylation of STAT5 in nearly all cell types, but this was completely abrogated by dasatinib in all but the plasmacytoid dendritic cells (Fig. 5C). This time, dasatinib had no effect on the exclusive induction of STAT5 phosphorylation shown in T cells by IL-7 (Fig. 5C). The suppression of pSTAT5 in PVO4-treated myeloid cells (Fig. 5C) supports the alternative mechanism of SrcFK activation of STAT5 activity and resembles the effect of dasatinib in a BCR-ABL–positive CML cell line (54).

That these pleiotropic downstream signaling molecules (ERK, STAT5, and PLCγ2) can be potently activated via both dasatinib-sensitive and -insensitive pathways highlights the drug’s context and cell-type specificity for different signaling mediators. Thus, the unchecked endogenous tyrosine kinase activity revealed by PVO4 may unveil differences in druggable signaling architecture between cell types by mimicking the dysregulated signaling of cells susceptible to disease or dysfunction. Additionally, as underlined by these limited examples, the dasatinib data set provided a mechanistic blueprint of regulatory cell signaling events that could potentially be exploited in later clinical applications.

Given the diverse cell type–specific effects of dasatinib, we investigated whether drug sensitivity would follow immunophenotypic progressions as observed above (Fig. 4). Cells representing HSC through pre-B I phenotypes were selected from the SPADE plot and subjected to principal component analysis (Fig. 5D). Unsupervised PCA detected the phenotypic progression in early B cell development as a single linear progression axis defined primarily by CD19, CD34, and CD33 intensities (Fig. 5E). This axis recapitulated the expected developmental sequence and independently verified the ordering identified by the SPADE algorithm (Figs. 2 and ​and5D).5D). The basal intensities of four intracellular signaling mediators (pSHP2, pCREB, pSrcFK, and p-p38) were unchanged throughout the PCA progression axis (Fig. 5F) and exhibited similar potential for activation by PVO4 (Fig. 5G). In contrast, these same signaling mediators exhibited a gradual increase in sensitivity to dasatinib correlated with maturation (Fig. 5H). This observation may be attributable to high expression of drug efflux pumps in the most immature cell types (HSCs and MPPs) (55). Alternatively, because PVO4 reveals endogenous kinase activity by repressing tyrosine phosphatases these observations may indicate a gradual shift in the phosphatase/kinase balance during early B cell maturation. Altogether, this approach provides insight into how a high dimensional analysis can call attention to potential off-target effects and new therapeutic opportunities that can be leveraged at all stages of the drug development pipeline.

The single-cell functional outcome data set (free to explore and available online) both confirmed expected immunological phenomena and yielded unexpected observations related to the spectrum of cell type–specific signaling faculties and drug responses that arise during hematopoietic development. For instance, there was a lack of IL-3 regulation of STAT3 in mature B cells, despite the presence of CD123 (IL-3 receptor–α chain) (Fig. 3). Additionally, both precise and more gradual continuous signaling transitions observed in the data set across developing cell subtypes (Fig. 3) represent some of the most interesting biological insights. Many of these precise transitions correlate well with receptor expression measured here [IL3/CD123 and LPS/CD14 (Fig. 3 and figs. S4 and S8) and known from previous work (IL7/pSTAT5 based on IL7 receptor expression on T cells (56)]. As for the continuous transitions, more broadly acting conditions such as PVO4 treatment revealed more subtle phosphorylation changes (Figs. 3 to ​to5)5) that probably reflect equally subtle changes in the kinase/phosphatase expression levels upstream of each of these measured targets, paralleling the phenotypic transitions.